Air transportation systems and methods

ABSTRACT

Systems and methods are disclosed for transporting people using air vehicles.

BACKGROUND

The present invention relates to air vehicles.

Smart cars offer consumers extra options on the road. However,well-publicized accidents and missteps show that even at a limitedautonomy level, AI systems struggle on the road.

Unless expressly identified as being publicly or well known, mentionherein of techniques and concepts, including for context, definitions,or comparison purposes, should not be construed as an admission thatsuch techniques and concepts are previously publicly known or otherwisepart of the prior art. All references cited herein (if any), includingpatents, patent applications, and publications, are hereby incorporatedby reference in their entireties, whether specifically incorporated ornot, for all purposes.

Synopsis

The invention may be implemented in numerous ways, e.g., as a process,an article of manufacture, an apparatus, a system, a composition ofmatter, and a computer readable medium such as a computer readablestorage medium (e.g., media in an optical and/or magnetic mass storagedevice such as a disk, an integrated circuit having non-volatile storagesuch as flash storage), or a computer network wherein programinstructions are sent over optical or electronic communication links.The Detailed Description provides an exposition of one or moreembodiments of the invention that enable improvements in cost,profitability, performance, efficiency, and utility of use in the fieldidentified above. The Detailed Description includes an Introduction tofacilitate understanding of the remainder of the Detailed Description.The Introduction includes Example Embodiments of one or more of systems,methods, articles of manufacture, and computer readable media inaccordance with concepts described herein. As is discussed in moredetail in the Conclusions, the invention encompasses all possiblemodifications and variations within the scope of the issued claims. Thefollowing presents a simplified summary of one or more aspects in orderto provide a basic understanding of such aspects. This summary is not anextensive overview of all contemplated aspects, and is intended toneither identify key or critical elements of all aspects nor delineatethe scope of any or all aspects. Its purpose is to present some conceptsof one or more aspects in a simplified form as a prelude to the moredetailed description that is presented later.

In one aspect, a flying vehicle includes a cab having a moveableactuator coupled to the propulsion unit to move the propulsion unitbetween a first position above the cab during lift-off and a secondposition during lateral (forward or backward) flight. In another aspect,a method for controlling a vehicle, the method comprising: generating amulti-dimensional model of a vehicle operating in a 3D environment;determining a hand control gesture as captured by a plurality of camerasor sensors in the vehicle; wherein a sequence of finger, palm or handmovements represents a vehicle control request; determining vehiclecontrol options based on the model, a current state of the vehicle andthe environment of the vehicle; and controlling the vehicle to operatebased on the model and the 3D environment. Based on LIDAR, radar, andcamera images, the system can generate 3D models for navigationpurposes. The 3D models are then crowd-sourced to the cloud and a highresolution 3D map of the region above the ground is generated. In oneaspect, a method for controlling a vehicle, the method comprising:generating a multi-dimensional model of a vehicle operating in a 3Denvironment; determining a hand control gesture as captured by aplurality of cameras or sensors in the vehicle, wherein a sequence offinger, palm or hand movements represents a vehicle control request;determining vehicle control options based on the model, a current stateof the vehicle and the environment of the vehicle; and controlling thevehicle to operate based on the model and the 3D environment. Based onLIDAR, radar, and camera images, the system can generate 3D models fornavigation purposes. The 3D models are then crowd-sourced to the cloudand a high resolution 3D map of the region above the ground isgenerated. In another aspect, a mapping system for an air spaceincludes: a plurality of air vehicles each having a plurality ofenvironmental sensors; a processor in at least one vehicle or in atleast one communication tower (edge processor) to receive sensor dataand create a 3D model of the air space from successive air vehiclesensor outputs. In another aspect, processors and sensor located on 5Gtowers provide low latency edge processing capability, including machinelearning processors to keep cost of vehicle low. Slice processing canalso be done, where a slice of the network is dedicated tocommunications between vehicles. Numerous other aspects are discussedbelow as well.

BRIEF DESCRIPTION OF THE FIGURES

FIGS. 1A-1H show exemplary flying vehicles, vehicle controllers, andcontrol processes.

FIG. 1I shows an exemplary network environment supporting the vehicle.

FIG. 1J shows an exemplary flight plan and approval process.

FIG. 1K shows exemplary lateral, vertical, and speed profiles of theplanned trip.

FIG. 1L shows an exemplary obstacle detection process.

FIGS. 2A-2B show exemplary long range rail system for transporting thevehicles.

FIG. 2C shows an exemplary travel process using a combination of railsand vehicles.

FIG. 3A shows an exemplary transportation marketplace.

FIGS. 3B-3I show various exemplary load/vehicle match systems.

FIGS. 4A-4C show an exemplary air delivery vehicle/drone.

FIG. 4D shows an exemplary loading system for the deliveryvehicles/drones in a warehouse, for example.

FIGS. 4E-4F show an exemplary process to use a combination of rail andair vehicle/drone to deliver packages to the door of customers.

FIGS. 5A-5D show exemplary processes to match delivery vehicles toloads.

FIG. 6 shows an exemplary edge processing system communicating over UWBtransceivers.

FIG. 7A show exemplary neural control of 5G planes, while FIGS. 7B-7Cshow exemplary neural network architectures.

FIG. 8 shows an exemplary transportation process.

DETAILED DESCRIPTION

This introduction is included only to facilitate the more rapidunderstanding of the Detailed Description; the invention is not limitedto the concepts presented in the introduction (including explicitexamples, if any), as the paragraphs of any introduction are necessarilyan abridged view of the entire subject and are not meant to be anexhaustive or restrictive description. For example, the introductionthat follows provides overview information limited by space andorganization to only certain embodiments. There are many otherembodiments, including those to which claims will ultimately be drawn,discussed throughout the balance of the specification.

Example embodiments, including some enumerated as Example Combinations(or ECs), are detailed for additional description of a variety ofembodiment types in accordance with the concepts described herein; theseexamples are not meant to be mutually exclusive, exhaustive, orrestrictive; and the invention is not limited to these exampleembodiments but rather encompasses all possible modifications andvariations within the scope of the issued claims and their equivalents.In the specification, reference may be made to the spatial relationshipsbetween various components and to the spatial orientation of variousaspects of components as the devices are depicted in the attacheddrawings, but the components may be positioned in any desiredorientation.

Flying Vehicle Architecture

In one aspect, a flying vehicle includes: a cab with an optionalpassenger seat with optional steering control; a propulsion unit havinga rotating blade and an engine to rotate the blade; a rail from a cabtop extending toward one external side of the cab, the cab having amoveable actuator coupled to the propulsion unit to move the propulsionunit between a first position above the cab during lift-off and a secondposition during lateral (forward or backward) flight.

Implementations may include one or more of the following. The propulsioncan be a propeller driven or jet driven unit. The engine can beelectric, gas, fuel cell, or hybrid. The windshield can be an AR port, atouch screen, or combination thereof. The blades or wings can be folded.The vehicle can be stored compactly. For lift off, the blades move froma storage position to the top of cab, extends itself a predeterminedheight above the other vehicles, and the blades rotate to lift the unitabove the other parked unit, and then moves to the side of the vehiclewhile retracting toward the sidewall of the vehicle for normal flightoperation to allow compact storage.

FIG. 1A-1B show an exemplary flying vehicle 10 that includes a frame orcab 11 with a passenger seat 19 with optional steering control 18.Besides being used to transport passengers, vehicle 10 may be used totransport parcels which may be loaded in at an approved parcel bay by aqualified loader. For parcel transport, numerous cabins can be linkedtogether for increased capacity. The vehicle 10 has a propulsion unit 12having a rotating blade and an engine to rotate the blade. The vehicleincludes a rail 16 from a cab top extending toward one external side ofthe cab. The rail or the cab has a moveable actuator coupled to thepropulsion unit 12 to move the propulsion unit 12 between a firstposition above the cab during lift-off and a second position behind orin front of the passenger during forward flight or backward flight. Aview port 14 can be provided with touchscreen capability to receive userinput. The view port 14 can be a windshield with projectors to provideaugmented reality. FIG. 1A shows a lift-off configuration, while FIG. 1Bshows a forward thrust configuration. In either configuration, a bottompropulsion unit provides lift as needed during take-off and is turnedoff during forward motion to conserve energy.

The engines of the propulsion unit 12 are preferably electric motors forquiet operation, or they may be liquid fuel powered engines such as fuelcell, gasoline, jet fuel or diesel powered engines including rotaryengines such as dual rotor or tri rotor engines or other highpower-to-weight ratio engines. Alternatively, some or all of the enginesof propulsion may be electric motors operated responsive to adistributed electrical system wherein battery systems are housed withineach nacelle or wherein electrical power is supplied to the electricmotors from a common electrical source integral to or carried by theframe. As another alternative, some or all of the engines of propulsionunit 12 may be hydraulic motors operated responsive to distributedhydraulic fluid system wherein high pressure hydraulic sources orgenerators are provided or a common hydraulic fluid system integral toor carried by the vehicle with cab 11.

The cab 11 includes a cockpit display device configured to displayinformation to an onboard pilot. Cockpit display device may beconfigured in any suitable form, including, for example, as one or moredisplay screens such as liquid crystal displays, light emitting diodedisplays and the like or any other suitable display type including, forexample, a display panel or dashboard display. Audio output and inputdevices such as a microphone, speakers and/or an audio port enable anonboard pilot to communicate with, for example, an operator at atransportation services provider facility. Cockpit display device mayalso serve as a pilot input device if a touch screen displayimplementation is used, however, other user interface devices mayalternatively be used to allow an onboard pilot to provide controlcommands to a vehicle being operated responsive to onboard pilot controlincluding, for example, a control panel, mechanical control devices orother control devices. As should be apparent to those that are skilledin the art, the vehicle 10 can be operated responsive to a flightcontrol protocol including autonomous flight control, remote flightcontrol, onboard pilot flight control and combinations thereof.

As vehicle 10 transitions from vertical takeoff and landing mode toforward flight mode, vehicle 10 is maintained in a generally horizontalattitude for the safety and comfort of passengers, crew and/or cargocarried in cab 11. This may be achieved due to the shape and the centerof gravity of cab 11 wherein aerodynamic forces and gravity tend to biascab 11 toward the generally horizontal attitude. Alternatively, oradditionally, a gear assembly, a clutch assembly or other suitablycontrollable rotating assembly may be utilized that allows for pilotcontrolled, remote controlled or autonomously controlled rotation of cab11 relative to vehicle 10 as vehicle 10 transitions from verticaltakeoff and landing mode to forward flight mode. Once vehicle 10 hascompleted the transition to forward flight mode, it may be desirable toadjust the center of gravity of the aircraft to improve its stabilityand efficiency. Once cab 11 is in the desired forward position, certainpropulsion assemblies of vehicle 10 may be shut down as the thrustrequirements in forward flight mode are reduced compared to the thrustrequirements of vertical takeoff and landing mode. When vehicle 10begins its approaches to the destination, inboard propulsion of vehicle10 are reengaged to provide full propulsion capabilities and the vehicle10 can begin its transition from forward flight mode to vertical takeoffand landing mode. During the transition from forward flight mode tovertical takeoff and landing mode, cab 11 is maintained in a generallyhorizontal attitude for the safety and comfort of passengers, crewand/or cargo carried in cab 11. Once vehicle 10 has completed thetransition to vertical takeoff and landing mode, vehicle 10 may completeits descent to a surface and cab 11 may now lower wheel assemblies (notshown) to provide ground support to cab 11.

In one embodiment, the blade can be folded for storage, and the foldingsystem may include, for example, a series of rapidly attachable “pitchlock” or flap lock assemblies, that together may form a flap locksystem, that are easily attached to the rotor hub of the helicopterduring a blade fold or unfold operation that remain in place fortransport or storage, and are fully removed prior to flight after theblades have been restored to flight position. In certain embodiments,these mechanisms may be attached to the hub using quick release pins anda piston-style toggle clamping mechanism, requiring no external toolsfor attachment. In potential addition to the flap lock assemblies, anoverhead lifting system support structure may be attached to the flaplock assemblies using similar quick-lock pins or other fasteners. Thisoverhead lifting system support structure then attaches to a bladesupport beam assembly that is used to attach to a blade using anappropriate blade clamp and then used to lift or lower the main rotorblades as necessary to remove loads transmitted to the blade pinsbecause of their static weight and other forces. More details are shownin U.S. Pat. No. 10,287,009, the content of which is incorporated byreference. Further, when there are many other vehicles parked, when thevehicle lifts off, the blade can be extended above other vehicles duringtake-off, and then be retracted thereafter to save storage space.

A process for transporting the vehicle 10 performed by the flightcontrol system of the vehicle is seen in FIG. 1C. The process includes:Upload a flight plan to the flight control system of the vehicle and getauthorization (21); Lift vehicle into the air in a vertical takeoff andlanding mode (22); Transition the vehicle from the vertical takeoff andlanding mode to a forward flight mode (23); Transport the vehicle to thedesired destination location (24); Transition vehicle from forwardflight mode to vertical takeoff and landing mode (25); and Land vehicleat destination (26)

The first step involves uploading a flight plan to the flight controlsystem of the vehicle 10 in 21. The authorization comes from a networkof ground station ATCs, as detailed below. Once approved, the vehiclemay then be operated responsive to autonomous flight control, remoteflight control or a combination thereof. The vehicle then goes into theair in a vertical takeoff and landing mode, as indicated in block 22.During the vertical takeoff, the vehicle is preferably maintained in agenerally horizontal attitude and each of the propulsion assemblies ofthe distributed propulsion system are independently operated using, forexample, selective collective pitch and selective thrust vectoring asdiscussed herein. Once the vehicle has reached a desired altitude invertical takeoff and landing mode, the next step is transitioning thevehicle from the vertical takeoff and landing mode to a forward flightmode, as indicate in block 23. Preferably, this transition involvesrotating the vehicle to remain in the generally horizontal attitude.Once in forward flight mode, the next step is transporting the vehicleto the desired destination location, as indicated in block 24. Dependingupon factors such as the distance of travel and environmentalconditions, it may be desirable to shut down certain propulsionassemblies, as discussed herein, during forward flight. As the vehicleapproaches the destination, the next step is transitioning the vehiclefrom the forward flight mode to the vertical takeoff and landing mode,as indicated in block 25. Preferably, this transition involves keepingthe vehicle remains in the generally horizontal attitude. The next stepis landing the vehicle at the destination, as indicated in block 26.This step may involve identifying a landing zone and performing anapproach in the vertical takeoff and landing mode.

Foldable Wing

In one aspect, a vehicle includes: a cab with an optional passenger seatwith optional steering control; a propulsion unit having a rotatingblade and an engine to rotate the blade; a wing coupled to the vehiclehaving a folded arrangement for storage and an extended position forflight, the cab having a moveable actuator coupled to the propulsionunit to move the propulsion unit between a first position above the cabduring lift-off and a second position during lateral flight.

Implementations may include one or more of the following. As shown inFIG. 1D, a pair of foldable, extensible wings 9 are connected to eachside the main body of the vehicle 10 can convert the vehicle 10 into aplane. Each wing is multiply folded so that when fully extended, thewing is at least 2 times the vehicle width, 3 times the vehicle width, 4times the vehicle width, 5 times the vehicle width, 6 times the vehiclewidth, 7 times the vehicle width in one embodiment, and 10 times vehiclewidth in various embodiments. In operation, for deploying the vehicle 10from a vertical state to forward flight state, wing 9 rotates around anaxis of a pivot pin for example counterclockwise until the wing ispositioned perpendicular to the vehicle body and then the portions ofthe wing are unfolded using actuators to arrive at a fully extendedposition. A locking mechanism as known in the mechanical art can be usedto place the foldable wings in a fully deployed position and to maintainthe offset-x shaped wings during flight. In a fully deployed positioneach wing is arranged to be perpendicular to the vehicle body and eachwing is arranged also perpendicular to its adjacent wing in oneembodiment. The foldable/collapsible wings, propellers, stabilizers andflight control surfaces decrease the bulk of the vehicle during storage.The vehicle with foldable/collapsible wings, flight control surfaces,stabilizers, propellers and means to control and drive the wings, flightcontrol surfaces, stabilizers, propellers and flight control surface cando rapid flights and rapid maneuvers/speed changes when the vehicle 10is in a flight state. The wing can be a main wing or tail wing.

Amphibious Vehicle

In one aspect, a vehicle includes: a cab with an optional passenger seatwith optional steering control; a propulsion unit having a rotatingblade and an engine to rotate the blade; a float coupled to the cab; thecab having a moveable actuator coupled to the propulsion unit to movethe propulsion unit between a first position above the cab duringlift-off and a second position during lateral flight.

Implementations may include one or more of the following. For amphibiousoperation shown in FIG. 1E, the float can be pontoons 13 or outriggerfloats which are used to provide lateral stability so as to avoiddipping a wingtip, which can destroy an aircraft if it happens at speed,or can cause the wingtip to fill with water and sink if stationary.Other embodiments use stub wings called sponsons, mounted with their ownlower surfaces nearly even with the ventral “boat-hull” shaped fuselagesurface to provide the needed stability, while floatplane amphibiansusually avoid the problem by dividing their buoyancy requirementsbetween two floats, much like a catamaran.

Flying Car

In one aspect, a vehicle includes: a cab with an optional passenger seatwith optional steering control; a propulsion unit having a rotatingblade and an engine to rotate the blade; wheel(s) coupled to the cab;the cab having a moveable actuator coupled to the propulsion unit tomove the propulsion unit between a first position above the cab duringlift-off and a second position during lateral flight.

Implementations may include one or more of the following. FIG. 1F showsa car version with wheels 7. In this embodiment, the wheels aremotorized and can be controlled by steering wheel for short rangedriving if needed. While the above discussion shows architectural blockdiagrams of the air vehicle, the shape of the vehicle can beaerodynamically optimized, such as that of FIG. 1G, for example.

Vehicle Sensors and Controllers

In one aspect, a vehicle includes: a cab with an optional passenger seatwith optional steering control; a propulsion unit having a rotatingblade and an engine to rotate the blade; autonomous flight electronicsto control the propulsion unit; the cab having a moveable actuatorcoupled to the propulsion unit to move the propulsion unit between afirst position above the cab during lift-off and a second positionduring lateral flight.

Implementations may include one or more of the following. FIG. 1H showsexemplary control system for the vehicle 10. The vehicle 10 includes aflight control system 80 that may be disposed within the cab 11 thatcommunicates with the electronics nodes of each propulsion assemblyreceiving sensor data from and sending flight command information to theelectronics nodes, thereby individually and independently controllingand operating each propulsion assembly. The flight control system 80receives data from various sensors such as air speed sensor 81, inertialsensor 82, radar 83, LIDAR 84, a plurality of cameras 85 positioned atvarious spots on the vehicle, accelerometer/gyroscope 86, and altimeterto determine height. The sensors provide orientation measurements andreadings, including pitch angle, roll angle and heading of the vehicleand inertial measurements, including accelerations and angular rates ofthe vehicle. The control system 80 in turn drives the motors 87 with thepropellers. The control system 80 also actuates the control surfaces(foldable wings, canards, etc) through actuator(s) 86. The controlsystem receives communications from cellular, WiFi or 802.X protocol,and VHF/UHF transceivers through transceiver 87. Cellular communicationcan be 4G, 5G, or 6G, for example. Power for the vehicle is managed byan energy manager 88. In operation, the flight control system 80 underthe control of flight control software, senses the flight controlsensors and moves the control surfaces using the control surfaceactuators to maintain the vehicle on a desired trajectory. The vehiclecan be guided via the cameras, radar, lidar, and can also be directed tocoordinates using the Global Positioning System (GPS). Each vehicle 10is configured with a unique identifier, such as a SIM card or the like.Similar to standard mobile phones, each device 10 is configured tomaintain an association with a plurality of cell towers 90 based on acurrent geographic location. Using triangulation or other locationidentification techniques (GPS, GLONASS, etc.), the location, altitude,speed, and direction of each vehicle 10 can be continuously monitoredand reported back to the servers to manage this data in real-time in anautomated fashion to track and control all vehicles 10 in a geographicregion. For example, the servers can manage and store the data in thedata store.

In an embodiment, at least one vehicle may comprise various componentsincluding, but not limited to, an engine, a power source such as abattery, a memory unit, a clock, a Global Positioning System (GPS), acompass and a configuration of sensors. Specifically, the battery mayprovide electrical power to the various components of the at least onevehicle. In one embodiment, the configuration of sensors may comprisealtimeters, accelerometers, gyroscopes, magnetometers, infrared sensors,LiDAR sensors, corona detectors, radiation detectors and so forth.According to an embodiment, the flight parameters may include at leastone of a temperature of the engine of vehicle, a power consumption ofthe vehicle, a charging level of the battery of the vehicle, and sensordata measured by the configuration of sensors of the vehicle. In anembodiment, the plurality of flight parameters may further includeGlobal Positioning System parameters (or GPS parameters) of the at leastone vehicle. In an example, the statistical data may pertain to the GPSparameters obtained using the Global Positioning System (GPS) of the atleast one vehicle. Examples of the GPS parameters include, but are notlimited to, latitudinal and longitudinal coordinates of the at least onevehicle, uncertainty of estimated geo location of the at least onevehicle, and number of GPS satellites visible to the at least onevehicle for identifying the geo location thereof. In an example, thestatistical data may pertain parameters and measurements of the radiocommunication path between the vehicle and GCS such as latency, signalstrength, bandwidth, jitter and/or bit error rate. In another example,the statistical data may comprise navigation data of the at least onevehicle. Specifically, the navigation data may comprise variousparameters captured through the flight duration such as compass reading,accelerometer reading, height above ground, angular velocity, angularacceleration, magnetic field and so forth.

During the flight, the ATC 89 can provide feedback such as speed,altitude, and heading, and the feedback can further include one or moreof temperature, humidity, wind, and detected obstructions. Theinstructions, when executed, can further cause the one or moreprocessors to: provide updates to the flying lane based on the feedbackand based on feedback from other devices. The instructions, whenexecuted, can further cause the one or more processors to based on thefeedback, determine the one or more vehicles 10 at ready to descend orfly to the destination and providing authorization to the one or morevehicles 10 for a descent. The instructions, when executed, can furthercause the one or more processors to—based on the feedback—detect a newobstruction and update the flying lane based on adjustments made by theone or more vehicles 10 due to the obstruction. The adjustments and/orthe updated flying lane can include a buffer distance from the newobstruction. The new obstruction can be detected by the one or morevehicles 10 based on hardware thereon and communicated to the ATC.

3D Models for Navigation

In one aspect, a method for controlling a vehicle, the methodcomprising: generating a multi-dimensional model of a vehicle operatingin a 3D environment; determining a hand control gesture as captured by aplurality of cameras or sensors in the vehicle, wherein a sequence offinger, palm or hand movements represents a vehicle control request;determining vehicle control options based on the model, a current stateof the vehicle and the environment of the vehicle; and controlling thevehicle to operate based on the model and the 3D environment. Based onLIDAR, radar, and camera images, the system can generate 3D models fornavigation purposes. The 3D models are then crowd-sourced to the cloudand a high resolution 3D map of the region above the ground isgenerated.

Implementations may include one or more of the following. In anotheraspect, a vehicle includes: a cab with an optional passenger seat withoptional steering control; a propulsion unit having a rotating blade andan engine to rotate the blade; sensors to capture 3D data around thevehicle; a processor on-board the vehicle or located near acommunication tower to receive sensor outputs and create a highdefinition 3D map; the cab having a moveable actuator coupled to thepropulsion unit to move the propulsion unit between a first positionabove the cab during lift-off and a second position during lateralflight.

FIG. 1L illustrates an exemplary process to fuse data for 3D models usedfor car navigation. FIG. 1L shows an exemplary system that performs datafusion based on sensor based detection of objects, change in weather andtraffic, and holiday/emergency conditions, among others. The processchecks all the sensors for change in weather (2004), detection of object(2002) and the GPS for current traffic conditions (2006). For each givensensor for detecting objects in a vehicle's environment, the processgenerates a 3D model of the given sensor's field of view; obstacleinformation from front cars using vehicle-vehicle communication (DRSC);neighboring car driver preference information; traffic informationincluding emergency information. The process can adjust one or morecharacteristics of the plurality of 3D models based on the receivedweather information to account for an impact of the actual or expectedweather conditions on one or more of the plurality of sensors. After theadjusting, aggregating, by a processor, the plurality of 3D models togenerate a comprehensive 3D model; combining the comprehensive 3D modelwith detailed map information; and using the combined comprehensive 3Dmodel with detailed map information to maneuver the vehicle. In FIG. 7A,the process checks sensors for object detection (2008) and then checksfor confirmations from other vehicles over V2V communication such asDSRC and then generates 3D model therefrom. The process can also checkfor weather change (2004) and correlate the weather change to generatean updated 3D model. Similarly, the process integrates traffic flowinformation (2006) and updates the 3D model as needed.

Obstacle Recognition

In one aspect, an obstacle detection system for an air space includes:one or more air vehicles each having a plurality of environmentalsensors; a processor with a neural network in at least one vehicle or inat least one communication tower (edge processor) to receive sensor dataand identify the obstacle in the air space from sensor outputs.

In another aspect, a method for controlling a vehicle includesgenerating a multi-dimensional model of a vehicle operating in a 3Denvironment; identifying an obstacle; determining vehicle controloptions based on the model, a current state of the vehicle and theenvironment of the vehicle; and controlling the vehicle to operate basedon the model and the 3D environment. Based on LIDAR, radar, and cameraimages, the system can generate 3D models for navigation purposes.

Implementations may include one or more of the following. The obstaclecan be detected by radar when the vehicle is in an approved lane that isnot aware of the obstacle in advance. Non-limiting examples of staticobstructions which are permanent include buildings, mountains, celltowers, utility lines, bridges, etc. Non-limiting examples of staticobstructions which are temporary include tents, parked utility vehicles,etc. Temporary and permanent static obstructions can be managed the samewith the temporary obstructions having a Time To Remove (TTR) parameterwhich can remove it from the database. Moving vehicles 10 are oneexample of dynamic obstructions. The ATC 89 can notify the vehicles 10of other vehicles 10 and the vehicles 10 can also communicate thedetection of the vehicles 10 as well as other dynamic and staticobstructions to the ATC. The obstructions can include dynamicobstructions, and the characteristics comprise size, shape, speed,direction, altitude, and heading. The characteristics can be determinedbased on analyzing multiple images or video over time. The vehicle 10method 1200 can further include receiving notifications from the airtraffic control system related to previously detected obstructions; andupdating the air traffic control system based on the detection of thepreviously detected obstructions. The characteristics are for anobstruction database maintained by the air traffic control system.

The vehicle's cameras, along with radar and lidar, automatically captureand classify dynamic/static obstacles encountered and classify theobstacles using neural networks (NN). The NN receives camera imagesalong with radar/lidar data to help distinguish obstacles to avoidduring the flight. One NN used is generative adversarial networks (GANs)are deep neural net architectures comprised of two nets, pitting oneagainst the other (thus the “adversarial”). One neural network, calledthe generator, generates new data instances, while the other, thediscriminator, evaluates them for authenticity; i.e. the discriminatordecides whether each instance of data that it reviews belongs to theactual training dataset or not. The generator is creating new, syntheticimages that it passes to the discriminator. It does so in the hopes thatthey, too, will be deemed authentic, even though they are fake. The goalof the generator is to generate passable obstacles. The goal of thediscriminator is to identify images coming from the generator asobstacles. Here are the steps a GAN takes: The generator takes in randomnumbers and returns an image. This generated image is fed into thediscriminator alongside a stream of images taken from the actual,ground-truth dataset. The discriminator takes in both real and fakeimages and returns probabilities, a number between 0 and 1, with 1representing a prediction of authenticity and 0 representing fake,resulting in a double feedback loop. The discriminator is in a feedbackloop with the ground truth of the images. The generator is in a feedbackloop with the discriminator.

In yet another aspect, a flight obstacle detector includes: sensorsincluding radar or lidar; a noise vector provided to a generator networkto provide synthesized obstacles; camera(s) providing images; adiscriminator network coupled to the camera and the generator network,the discriminator network and generator network iteratively trained toidentify a flight obstacle; abstract the obstacle to a bounding box suchas a pyramid (mountain), cylinder (building), rectangle (building) withparameters of each object and location and height of the obstacle;search obstacle DB based on location and parameters and if no entrymatches the obstacle, then add a new entry with the abstraction alongwith location/height of obstacle, and an image and 360 deg video.

The systems and methods provide a mechanism in the ATC 89 tocharacterize detected obstructions at or near the ground. In anembodiment, the detected obstructions are dynamic obstructions, i.e.,moving at or near the ground. Examples of dynamic obstructions caninclude, without limitation, other vehicles 10, vehicles on the ground,cranes on the ground, and the like. Generally, dynamic obstructionmanagement includes managing other vehicles 10 at or near the ground andmanaging objects on the ground which are moving which could eitherinterfere with landing or with low-flying vehicles 10. In an embodiment,the vehicles 10 are equipped to locally detect and identify dynamicobstructions for avoidance thereof and to notify the ATC 89 formanagement thereof to update the obstacle database.

Further, the detected obstructions can be static obstructions, i.e., notmoving, which can be temporary or permanent. The ATC 89 can accuratelydefine the location of the detected obstructions, for example, a virtualrectangle, pyramid, cylinder, etc. defined by location coordinates andaltitude. The defined location can be managed and determined between theATC 89 and the vehicles 10 as well as communicated to the vehicles 10for flight avoidance. That is, the defined location can be a “no-fly”zone for the vehicles 10. Importantly, the defined location can beprecise since it is expected that there are a significant number ofobstructions at or near the ground and the vehicles 10 need tocoordinate their flight to avoid these obstructions. In this manner, thesystems and methods seek to minimize the no-fly zones.

Further, the present disclosure relates to obstruction detection systemsand methods with air traffic control systems for vehicles 10.Specifically, the systems and methods use a framework of an air trafficcontrol system which uses wireless (cell) networks to communicate withvarious vehicles 10. Through such communication, the air traffic controlsystem receives continuous updates related to existing obstructionswhether temporary or permanent, maintains a database of presentobstructions, and updates the various vehicles 10 with associatedobstructions in their flight plan. The systems and methods can furtherdirect vehicles 10 to investigate, capture data, and provide such datafor analysis to detect and identify obstructions for addition in thedatabase. The systems and methods can make use of the vast datacollection equipment on vehicles 10, such as cameras, radar, etc. toproperly identify and classify obstructions.

Through the data capture equipment, the vehicles 10 are adapted todetect potential obstructions and detect operational data (speed,direction, altitude, heading, location, etc.). For newly detectedobstacles, the vehicles 10 are adapted to transfer the operational datato the servers 92. Sensors on the vehicles 10 can capture identificationdata, photos, video, etc. and upload. In an embodiment, the vehicles 10are provided advanced notification of obstructions and capable of localdata processing of the identification data to verify the obstructions.If the local data processing determines an obstruction is already known,i.e., provided in a notification from the servers 92, the vehicle 10does not require any further processing or data transfer of theidentification data, i.e., this obstruction is already detected. On theother hand, if the vehicle 10 detects a potential obstruction, i.e., onethat it has not been notified of, based on the local data processing,the vehicle 10 can perform data transfer of newly identified obstructiondata to the servers 92.

The servers 92 are configured to manage the obstruction DB 820, namelyto update the entries therein. The servers 92 are configured to receiveoperational data from the vehicles 10 under control for managementthereof. Specifically, the servers 92 are configured to manage theflight plans of the vehicles 10 200, and, in particular with respect toobstructions, for advanced notification of future obstructions in theflight plan.

The servers 92 are configured to receive the detection of potentialobstructions. The vehicles 10 can either simply notify the servers 92 ofa potential obstruction as well as provide the identification data forthe servers 92 to perform identification and analysis. Upon receipt ofany data from the vehicles 10 200 related to obstructions (a merenotification, actual photos, etc.), the servers 92 are configured tocorrelate this data with the DB 820. If the data matches an entry thatexists in the DB, the servers 92 can update any information related tothe obstruction such as last seen date.

If the servers 92 detect that the potential obstruction does not existin the DB, the servers 92 add an entry in the DB, perform identificationif possible from the identification data, and potentially instruct avehicle 10 to identify in the future. For example, if the servers 92 canidentify the potential obstruction from the identification data, theservers 92 can create the DB entry and populate it with the identifieddata. The servers 92 can analyze the identification data, as well asrequest human review, using pattern recognition to identify what theobstruction is, what its characteristics are (height, size, permanency,etc.). If the servers 92 do not have enough identification data, theservers 92 can instruct the identifying vehicle 10 or another vehicle 10in proximity in the future to obtain specific identification data forthe purposes of identification.

The obstructions can be stored and managed in an obstruction database(DB) communicatively coupled to the servers 92 and part of the Airtraffic control system 300. Obstructions can be temporary or permanentand managed accordingly. Thus, the DB 820 can include an entry for eachobstruction with location (e.g., GPS coordinates), size (height), andpermanence. Temporary obstructions can be ones that are transient innature, such as a scaffold, construction equipment, other vehicles 10 inflight, etc. Permanent obstructions can be buildings, power lines, celltowers, geographic (mountains), etc. For the permanence, each entry inthe DB can either be marked as permanent or temporary with a Time toRemove (TTR). The TTR can be how long the entry remains in the DB. Thepermanence is determined by the servers 92 as described herein.

Crowd Sourcing 3D Models of Flight Lanes with Obstacles

In one aspect, a mapping system for an air space includes: a pluralityof air vehicles each having a plurality of environmental sensors; aprocessor in at least one vehicle or in at least one communication tower(edge processor) to receive sensor data and create a 3D model of the airspace from successive air vehicle sensor outputs.

Implementations may include one or more of the following. In anotherexemplary system for crowd-sourcing navigation data, a crowdsourcingserver is in communication with a plurality of vehicles 1 . . . n. Thevehicles performs peer-to-peer discovery and crowd-sourced navigation.The system receives proximity services for a group of vehicles travelinga predetermined route using peer-to-peer discovery, receivescrowdsourcing data from said plurality of vehicles, sharingcrowdsourcing data to the group of vehicles (or a subsequent group ofvehicles) traveling the route of interest. Such information can be usedin providing navigation guidance to the vehicle traveling the routeusing the crowdsourced data. In one aspect, the vehicles traveling thesame route can be determined using a vehicle to vehicle communicationprotocol that facilitate identifying peers based upon encoded signalsduring peer discovery in a peer to peer network. The system can be WiFior cellular based such as the Proximity Services, among others. In oneembodiment, the identification of peers based upon encoded signalsduring peer discovery in a peer to peer network can be done. Forexample, direct signaling that partitions a time-frequency resource intoa number of segments can be utilized to communicate an identifier withina peer discovery interval; thus, a particular segment selected fortransmission can signal a portion of the identifier, while the remaindercan be signaled based upon tones communicated within the selectedsegment. Moreover, a subset of symbols within the resource can bereserved (e.g., unused) to enable identifying and/or correcting timingoffset. Further, signaling can be effectuated over a plurality of peerdiscovery intervals such that partial identifiers communicated duringeach of the peer discovery intervals can be linked (e.g., based uponoverlapping bits and/or bloom filter information). The method caninclude transmitting a first partial identifier during a first peerdiscovery interval. Also, the method can comprise transmitting a secondpartial identifier during a second peer discovery interval. Further, themethod can include generating bloom filter information based upon thecombination of the first partial identifier and the second partialidentifier. Moreover, the method can comprise transmitting the bloomfilter information to enable a peer to link the first partial identifierand the second partial identifier. Another embodiment communicates usingLTE Direct, a device-to-device technology that enables discoveringthousands of devices and their services in the proximity of ^(˜)500 m,in a privacy sensitive and battery efficient way. This allows thediscovery to be “Always ON” and autonomous, without drasticallyaffecting the device battery life. LTE Direct uses radio signals—called‘expressions’—which can be private and discreet (targeted securely forcertain audiences only) or public (transmitted so that any applicationcan receive them). Public expressions are a common language available toany application to discover each other, and this is the door to consumerutility and adoption. Public expressions exponentially expand the fieldof value. For example, vehicles that share same driving segments canbroadcast expressions indicating their path(s). The system detectsvehicles in the same segment as part of the proximity services forcapturing and sharing crowd-sourced navigation data. Public expressionscombine all applications—all value—into one single network, therebyexpanding the utility of the system.

In one aspect, the process includes detecting the closing of a laneusing the crowdsourcing data; predicting an avoidance maneuver using thecrowdsourcing data; predicting a congestion with respect to a segment ofthe route of the at least one vehicle using the crowdsourcing data; andpredicting traffic light patterns using the crowdsourcing data.Implementation can include one of the following. The system candetermine the presence of obstacles in a flight lane by monitoring apattern of vehicle avoidance of a particular location of the lane. Theobstacles can be a new tower or smoke from a recent volcano eruption,among others. The vehicular avoidance information can be sent tovehicles that are planning to use that particular road section tooptimize. The system can share prior vehicle's avoidance maneuver bymonitoring change of vehicle direction and distance traveled at a closevicinity of a location on the route of a lead vehicle; and determiningan avoidance maneuver in response to a ratio of change of vehicledirection and distance traveled being less than a predeterminedthreshold value. The system can determine a route based at least in parton an amount of time predicted for travelling from a starting locationto a destination location of the route using the crowdsourcing data; anddetermining a route based at least in part on a predicted fuelconsumption of the route using the crowdsourcing data. The determininginformation corresponding to a route of interest to at least one vehiclefurther can include monitoring a distance traveled by the at least onevehicle after reaching a destination, and predicting availability ofparking spaces at the destination based at least in part on the distancetraveled; and monitoring an amount of time traveled by the at least onevehicle after reaching a destination, and predicting availability ofparking spaces at the destination based at least in part on the amountof time traveled. The determining information corresponding to a routeof interest to at least one vehicle further comprises: measuring a timetaken to travel a predefined percent of the route until the at least onevehicle comes to a halt at a predetermined location; and predicting anaverage amount of time used to find parking at the predeterminedlocation using the time taken to travel a predefined percent of theroute. The determining information corresponding to a route of interestto at least one vehicle further comprises at least one of determiningpopularity of a fueling station along the route; determining type offuel sold at the fueling station along the route; determining popularityof a business along the route; and determining popularity of a rest areaalong the route.

Next, a system to crowd-source the updates of precision maps with datafrom smart vehicles is detailed. In embodiments, crowd-sourced obstacledata can be used to update a map with precision. Obstacles can be trees,poles, new buildings, among others. Crowd-sourced information is updatedinto the map and annotated by time, weather and periodicity. Thedetected obstacle information may include a geographic location of thevehicle and a predetermined map of the lane. The computer system maydetermine the geographic location of the obstacle by, for example, usinga laser rangefinder or light detection and ranging (LIDAR) unit toestimate a distance from the obstacle to the at least two objects nearthe vehicle and determining the geographic location of the obstacleusing triangulation, for example. Such information is updated into themap system and marked as temporal. During use, if recent vehicles takedefensive driving around the temporary obstacle, the map adds theobstacles to the map for the route guidance module. If recent vehiclesdrive the lane as though the obstacle does not exist, the system removesthe obstacle from the map database, but keeps track of the history incase it is a periodic obstacle. The obstacle information is alsoreported to government agency for repair/maintenance. In anotherembodiment, if vehicles fly through the lane with a smooth line orcurve, but abruptly brakes, the system infers that the lane hasobstacles, for example, and the bad infrastructure is reported for pathplanning (to add more travel time, or to change the route to avoid thebad lane infrastructure if it is long. The new information is used toupdate a digital map that lacks the current information or that containsinaccuracies or may be incomplete. The digital map stored in the mapdatabase may be updated using the information processed by a mapmatching module, matched segment module, and unmatched segment module.The map matching module, once it has received obstacle location and GPStraces, processes obstacle locations and GPS traces by matching them toa lane defined in the digital map. The map matching module matches theobstacles and the GPS traces with the most likely lane positionscorresponding to a viable route through the digital map by using theprocessor to execute a matching algorithm. In one example, the matchingalgorithm may be a Viterbi matching algorithm. Where the GPS traces domatch a lane defined in the digital map, the matched trace to which theGPS traces match and obstacle information are sent to the matchedsegment module for further processing as will be described below. Wherethe GPS traces do not match a lane defined in the digital map, theunmatched trace to which the GPS traces are correlated with and theobstacle position information are sent to the unmatched segment modulefor further processing. The matched segment module and unmatched segmentmodule both provide metadata to the map updating module. The metadatamay include obstacle metadata lane geometry refinement metadata, laneclosure and reopening metadata, missing intersection metadata, missinglane data and one-way correction metadata. The map updating moduleupdates the digital map in the map database.

The process to update maps using crowd-sourced data may begin with theunmatched segment module clustering the unmatched GPS traces receivedfrom the map matching module. Many available algorithms may be suitablefor this process, but in one example, an agglomerative clusteringalgorithm that iteratively compares GPS traces with each other andcombines those that fall within a predetermined tolerance into a clustermay be used. One example of such and algorithm uses the Hausdorffdistance as its distance measure in the clustering algorithm. Once thecluster is selected, the unmatched segment module may produce a singlelane geometry for a cluster of unmatched GPS traces using a centerlinefitting procedure in which the single lane geometry describes a new lanesegment with the obstacle which is not described in the current mapdatabase. In one example, a polygonal principal curve algorithm or aTrace Clustering Algorithm (TCl) algorithm can be used. The digital mapcan be modified to include the new lane, including possibly newintersections in the base map and any associated pointers or indicesupdated.

Autonomous Navigation with Unplanned Events or Obstacles

In one aspect, an obstacle detection system for an air space includes:one or more air vehicles each having a plurality of environmentalsensors; a processor with a neural network in at least one vehicle or inat least one communication tower (edge processor) to receive sensor dataand avoid the obstacle in the air space from sensor outputs.

Implementations may include one or more of the following. The vehiclegenerally follows its planned path, which is initially a linear pathfrom point A to point B as guided by cameras, GPS, lidar/radar and theflight computer. The linear path becomes zig-zags or other non linearpaths to handle known obstacles and then the linear path is resumed asthat is the shortest distance, until the vehicle 10 encounters anunexpected obstacle as detected by cameras and sensors. Based mainly oncamera detection but also through lidar/radar, the system can detect newobstacles that require a workaround of the flight plan. Once theunexpected obstacle (not in the flight plan submitted for approval) isdetected, the system attempts to communicate with the obstacle usingvehicle-to-vehicle (“V2V”) communication. If there is no reply from theunknown obstacle, the obstacle may be a large bird or a human controlleddrone whose intent can't be ascertained. If so, the system providescollision avoidance by modeling potential collisions based on algorithmstaking into account vehicle size, speed, direction and wind load; andwind speed and direction. The operational data can include speed,direction, altitude, heading, and location of the vehicle, and whereinthe future flight plan can be determined based on the size of thevehicle 10 and the vehicle 10 speed, direction, and wind load. Theprocess can further include providing the flying lane assignment to thevehicle 10, wherein the flying lane assignment is selected from aplurality of flying lane assignments to maximize collision-freetrajectories based on the static or dynamic obstruction. The vehicle 10performs an evasive maneuver by slowing down, reversing course, movingabove or below the obstacle, or a combination thereof, and the processcan further include managing ground hold time for a plurality ofvehicles 10 to manage airspace, i.e., minimize ground hold time forvehicles, safely maximize flight time for all airspace users. Theevasive maneuver instructions utilize six degrees of freedom in movementof the vehicle. The changes can include instructions to changedirection, instructions to change flying lane(s), instruction to landand where the vehicle should target for landing, full route modificationwith an emphasis on route optimization while avoiding the negativeimpact of the conflicting vehicles, instructions to speed up or slowdown, instructions to change altitude, instructions to hold position fora specific or indefinite time period, instructions to move to a safeposition away from the potential collision and either hold in the air oron the ground for a specific or indefinite time period, instructions toland very quickly, instructions to land very slowly, instructions tocircle, and the like. When the obstacle is safely behind the vehicle 10,then the vehicle resumes its planned path.

The unplanned obstacle can be unexpected weather or terrorist attack,for example. The changes can include instructions to change direction,instructions to change flying lane(s), instruction to land and where thedrone should target for landing, full route modification with anemphasis on route optimization while avoiding the negative impact of theweather event, instructions to speed up or slow down, instructions tochange altitude, instructions to hold position for a specific orindefinite time period, instructions to move to a safe position awayfrom the weather event and either hold in the air or on the ground for aspecific or indefinite time period, instructions to land very quickly,instructions to land very slowly, instructions to circle, and the like.When the obstacle is safely behind the vehicle 10, the vehicle resumesits planned path.

If the obstacle can communicate via V2V, the vehicles can collaborate ina self-organizing traffic control system by creating an ad-hocvehicle-based network facilitated by V2V communication. In this context,V2V communication enables development of an inter-vehicle control plan(“IVCP”) that can resolve a travel-priority conflict in thepotential-conflict zone which, if left unresolved, could result in acollision for flying vehicles. Generally, An IVCP includes a set oftravel instructions that is communicated to vehicles participating inthe ad-hoc network for the particular potential-conflict zone. Forexample, these instructions can include a sequence by which vehiclesapproaching from different directions may proceed through apotential-conflict zone, the speed at which vehicles approaching aconflict zone should be traveling, the lateral/vertical profiles of theflying vehicle so that conflict may be avoided using 3D solutions, andso forth. One important aspect of the IVCP is that the instructions aretailored for the specific vehicles participating in a conflict and arealso coordinated with the other vehicles participating in the conflictso as to resolve the conflict without incident. Additionally, thiscoordination can assist with optimizing vehicle flow through apotential-travel-priority conflict zone as a function of traffic volume,flight lane conditions, known or predicted travel routes for vehiclesnear the conflict zone, and/or a priority status of one or more of suchvehicles.

The method for autonomous navigation with unknown obstacle handlingincludes: submit a flight plan handling known obstacles for approval;obtain approval and travel using approved flight lane; detect incomingunexpected obstacle using camera, lidar, radar, and other sensors;interrogate approaching obstacle with V2V communication; if no reply,take evasive action to bypass obstacle, and else select a trafficcoordinator TC (the selected communication tower or a lead vehicle ifthere is no communication tower); TC broadcasts status as the trafficcoordinator and establishes an inter-vehicle traffic control plan(“IVCP”); TC communicates IVCP to the other vehicles approaching thepotential-flight-conflict zone. Optionally, TC periodically re-broadcastits identity and re-broadcast the IVCP to confirm control of thepotential-conflict zone and inform any newly arrived vehicles.

In one embodiment, a V2V communications system may be designed andconfigured to receive signals from at least one other vehicle within thead-hoc vehicle-based network at issue that have the same or a similarV2V communications system. These signals can include informationcharacterizing the type of vehicle, its weight, its speed, relevanttraffic and weather conditions, the manner of approach of a vehicle,direction, latitude/longitude position, and a priority status for thevehicle, among many others. A V2V communications system may also bedesigned and configured to provide a communications link betweenvehicles approaching a potential travel-priority conflict zone in orderto elect a traffic coordinator (or leader), collect data, and performanalyses so as to create the IVCP, as well as to communicate the IVCP tothe participating vehicles.

In an example, if the obstacle causes a discontinuity to arise withrespect to the planned flight lanes of one or more vehicles in thead-hoc network, the system takes the following steps:

1. The discontinuity is identified;

2. Limitations are identified applicable to the end user's system andthe source;

3. A navigational database is accessed to determine known waypoints thatcan be used to remove the discontinuity;

4. Create unique waypoint and maneuver instructions specific to eachvehicle; and

5. Determine real time operational restrictions, and user preference, togenerate specific communications protocols to invoke a flightinformation message free from discontinuities for all vehicles in theadhoc network.

Solutions by the TC can use Time-Based Flow Management, or TBFM,predicts what time all the flights will get to the point in the airwhere they start to change travel path a predetermined time before theyget there. This key point in accurately predicting the arrival enablesthe TC to determine the most efficient schedule to get each flight tothis spot. TBFM then builds a 4-dimensional (latitude, longitude,altitude and time) trajectory for each flight. That is, it decides theexact times the vehicle needs to be at certain intermediate points alongthe way in order to get to its scheduled time to begin to make its pathchange. Controllers receive these scheduled times and guide the flightsso that each vehicle reaches its intermediate points at the right timewhile maintaining the required separation between the vehicles(time-based metering). The TC can delay flights slightly by assigningthem a slower speed or a different altitude. Time-based metering is moreeffective the more airspace controllers have available. Adjacent CenterMetering (ACM) increases the amount of airspace and time controllers canuse to maneuver aircraft to meet their scheduled times of arrival andexpands the benefits of time-based metering to aircraft that are fartheraway from the arrival airport.

Another implementation to solve the discontinuity facing members of theadhoc network is detailed next. This implementation plans a flight pathof an aircraft based on a pigeon-inspired optimization (PIO) method asfollows: establish trajectory prediction model with uncertainty;initialize the route to be optimized by the pigeon-inspired algorithmaccording to the route information in the specified area, and initializethe parameters such as the dimension D of the search space, pigeonpopulation, iteration number, and geomagnetic factor R in thepigeon-inspired optimization algorithm; set the speed and position ofeach pigeon at random, calculate the fitness value according to thefitness function, find the current optimal path, and store eachparameter of the current optimal path and solve the minimization problemof costs for a particular path; apply map and compass operator to updatethe speed and position of each pigeon; perform landmark operations, sortall pigeons according to fitness values, lower-adapted pigeons followthe adapted pigeons and find the center of the flock (destination), allpigeons will fly directly to their destination; determine whether themaximum number of iterations is reached, and if not, repeat theoperation of map and compass and landmark until the number of iterationsreaches the maximum number of iterations of landmark operator. Moredetails are in Application 20190035286, the content of which isincorporated by reference.

Once the traffic coordinator creates the IVCP and communicates tovehicles approaching a potential-travel-priority-conflict zone in theabove-described steps, the vehicles can then participate in the IVCP. Inone example, IVCP instructions are communicated to the vehiclesparticipating in the ad-hoc vehicle-based network corresponding to thepotential-travel-priority-conflict zone by providing each vehicle with avirtual traffic control to change speed, lateral/vertical profile of thevehicle.

There are occasions that all vehicles must yield to a priority vehicle(fire/ambulance, among others). The method includes resolving apotential vehicular travel-priority conflict by: communicating withapproaching vehicles so as to collect data relevant to an incipientconflict, creating an IVCP to avoid or resolve the conflict,communicating the IVCP to the vehicles participating in the potentialconflict, receiving a priority-request message from a priority vehicleproximate to the potential conflict, and transmitting a priority-grantedmessage to the priority vehicle.

The navigation may be implemented in the IVC system with a processor incommunication with a network that is generally: 1) programmed withinstructions for performing steps of a method of the present disclosure;2) capable of transmitting, receiving, and/or storing data necessary toexecute such steps; and 3) capable of providing any user interface thatmay be needed for a user to interact with the system, including settingthe system up for a vehicle priority managing session, among otherthings. Those skilled in the art will readily appreciate that aspects ofthe present disclosure can be implemented with and/or within any one ormore of numerous devices, ranging from self-contained devices, such asdedicated IVC devices that are either mobile or permanently mounted tovehicles, mobile phones, smartphones, tablet computers, laptopcomputers, to networks each having two or more of any of these devices,among others. Fundamentally, there is no limitation on the physicalconstruct of An IVC system, as long as it can provide one or more of thefeatures and functionality described herein. In some embodiments,depending on specific implementation, one or more steps of method and/orany other method(s) incorporating features/functionality disclosedherein may be implemented substantially in real-time. The network can bea Cellular V2X (C-V2X) is a 3GPP standard describing a technology toachieve the V2X requirements. C-V2X is an alternative to 802.11p, theIEEE specified standard for V2V and other forms of V2X communications.An alternative to cellular V2X technology is dedicated short-rangecommunications (“DSRC”) technology. The system can also use both or anyother V2V communication standards.

The TVC system may include, for example, a V2V communications system, aprocessor, TVC software, a physical memory, a user interface, and anoptional vehicle interface. These elements can be used together, inwhole or in part, to create an IVCP, communicate An IVCP to othervehicles, receive An IVCP from another vehicle, and execute theinstructions supplied by the IVCP, depending on the configuration of theIVC system and the needs of the particular IVCP ad-hoc vehicle-basednetwork under consideration. The IVC system can also optionally includean on-board location database and/or a travel-route database. The V2Vcommunications system may be configured to transmit and receive signalscommunicating IVCP instructions using any one or more of a variety ofprotocols. For example, a V2V communications system may broadcastsignals transmitting IVCP instructions periodically from a vehiclethrough a process known in the art as “beaconing.” As part of thebeaconing process, the information described above is communicated atregular intervals and throughout a given geographic area surrounding thevehicle performing the beaconing. Beaconing signals may include, forexample, velocity, heading, vehicle type, acceleration (using anin-vehicle accelerometer), vehicle priority status, a network address orother network identifier for the originator, a unique beacon-signalidentifier, a timestamp, a lane identifier, and/or an indication ofwhether the originator is currently a traffic coordinator, among others.In one specific example, beaconing can utilize a beacon packet with thefollowing composition: ∥Packet Type|Unique Packet ID|Timestamp|UniqueVehicle Address ID|Coordinates|Direction|Vertical Profile|HorizontalProfile|Speed|VTL Leader∥. These beaconing signals (e.g., packets) canbe received and/or retransmitted by another IVC system similar to theoriginating system through a V2V system. Furthermore, beaconing signalscan be used in cooperation with an onboard location database. The use ofa location database with periodically repeated beaconing signals canpermit an IVC system to track the location of proximate vehicles. Evenfurther, when a location database and beaconing signals are used alongwith a travel-route database, An IVC system can anticipatetravel-priority conflict zones because the system is informed of, at theminimum, the location and velocity of proximate vehicles in the contextof known travel-routes. In some examples, this can permit An IVC systemto adapt to local vehicle densities and to anticipate, and accommodate,density trends.

The V2V communications system may also or alternatively be designed andconfigured to transmit and receive signals using non-beaconing protocolsas well, such as signals transmitted to or from another proximatevehicle directly, for example using a handshake, push, or pull protocol,among others. Or, in yet another example, the above-described signalscan be communicated between vehicles using a method known in the art as“Geocasting.” In this method, vehicles can communicate with othervehicles regionally proximate but out of DSRC range by using interveningvehicles as transponders that propagate the DSRC signal. Those skilledin the art will appreciate that beaconing, Geocasting, and directtransmission are but a selection of the many existing techniques thatcan be used in connection with the teachings of the present disclosure.

As noted above, IVC system may optionally include a vehicle interfacethat can interact directly with the operative functionality of thevehicle, such as in a semi-autonomous or fully autonomous vehicle or inautonomous flying methods, thereby automatically implementing the IVCPlittle to no input from the vehicle operator, if any. For example, uponreceipt or creation of An IVCP, a vehicle interface may, throughoperative connections to the various vehicle systems (e.g., propulsion,steering, braking, directional signal, etc.) direct the vehicle toconform to the IVCP. A vehicle interface can also provide vehicle dataand information in order to better inform an IVC system in the creationof the IVCP. For example, a vehicle interface can provide velocity,heading, vertical/lateral profile, vehicle type, acceleration (using anin-vehicle accelerometer), vehicle priority status, and otherinformation relevant to the creation of the IVCP to a processor in theIVC system. This information can then be used by the processor incooperation with IVC software to create an IVCP. Of course, thisinformation may also be communicated via a V2V communications system toanother vehicle, such as one that has been elected as a trafficcoordinator and charged with creating the IVCP.

When two or more vehicles meet at a conflict zone, the IVC systems ofthe vehicles communicate with each other in order to establish an IVCPthat utilizes an ad-hoc communication network usable to resolvetravel-priority conflicts. In one example, the vehicles communicate witheach other using DSRC that can use IEEE 802.11(p) communication protocolvia DSRC-capable radios in order to receive and transmit relevantinformation. Other examples of methods by which vehicles can communicateinclude other radio-frequency communication protocols, cellularcommunications (including 1G-5G, etc.), Wi-Fi. Wi-Fi enabled internet,WiMAX, laser or other light-based communication or data transfer, andothers, as well as combinations thereof.

A variety of inputs can be used to identify anticipated priorityconflicts and establish the IVCP that is subsequently communicated tothe other vehicles approaching the travel-priority conflict zone. Forexample, one type of input includes vehicle-specific metrics. Suchmetrics may include, but are not limited to, velocity of travel,vertical/lateral profile, distance from the conflict zone, vehicleweight, indicia of traffic congestion, vehicle type, vehicle priority,and direction of travel. Other types of inputs can include knowntravel-route features stored in a travel-route database and/or predictedtravel-route features derived therefrom. Flying vehicles approaching apotential travel-conflict zone communicate with each other, using one ormore of the methods and systems described above, to get commands from adesignated cell tower that can provide a coordinated set of IVCPinstructions to vehicles participating in the ad-hoc vehicle-basednetwork established to avoid any real conflicts that could occur in thepotential travel-priority conflict (traffic coordinator). Alternatively,the traffic coordinator can be elected from among candidates in thead-hoc vehicle-based network based on any one or more of a number ofdifferent factors, including those factors that indicate the ability tostop safely before a conflict zone, the ability to influence the trafficflow through the conflict zone, the traffic density on the variousapproaches to the travel-priority conflict zone, past waiting times, andothers. For example, a subset of candidates for coordinators may beidentified as those leading their respective queue of vehicles on agiven approach to a priority-conflict zone. In this example, thesevehicles will be the first to arrive at the conflict zone, and aretherefore more likely to be in communicative contact with vehiclesapproaching the conflict zone from other directions. This arrangementfacilitates, but is not required for, V2V communication. Furthermore,those vehicles leading their respective queues can prevent the vehiclestrailing them from proceeding further, thereby controlling the vehiculartraffic flow if so required by the IVCP. Other factors that can be usedto elect the coordinator include, for example, the ability to hoversafely before entering the potential travel-priority-conflict zone, thepresence of possible barriers to V2V communication, a priority status ofone or more vehicles approaching the potential conflict zone, referredto herein as a “priority vehicle” (e.g., emergency-service vehicles,mass-transit vehicles, vehicles involved in a funeral procession, etc.),traffic planning policies favoring higher traffic flow in a givendirection.

In one embodiment to optimize traffic flow over a geographic areacontaining many actual, anticipated, or potential travel priorityconflicts, an intersection-based communication device/sensor can informthe IVC system by providing traffic-related information or by providingrecommended route information, as supplied by a central coordinator(ground control or lead vehicle, among others). For example, eitherthrough communication methods described above (including beaconing andGeocasting, among others), or through information collected directlyusing techniques well known to those skilled in the art, anintersection-based communication device/sensor can gauge the degree ofproximate congestion. This information can then be communicated usingany communication method known to those skilled in the art, includingboth wired and wireless techniques, to the central coordinator. Thecentral coordinator, having been provided with analogous informationfrom other travel-priority conflict zones over a geographic areacontaining a plurality of such zones, can provide one or moreintersection-based communication devices/sensors with, for example,recommended directions for some or all of associated IVCPs, which may bedetermined as a function of one or more priority vehicles'travel-routes, positions, and/or other information received from and/orotherwise regarding one or more priority vehicles. These recommendationscan then be communicated from the intersection-based communicationdevice/sensor to one or more WC systems using the techniques and methodspreviously described. Furthermore, the central coordinator can useinformation collected not only to provide information to An IVC systemto inform its decision making process, such as by providing a knownroute for a priority vehicle received from an independent entity, suchas a fire-house, police station, or municipal government, but thecentral coordinator can also dictate instructions to IVC systems,thereby centralizing coordination of traffic flow.

The traffic coordinator (the selected communication tower or a leadvehicle if there is no communication tower) can broadcast its status asthe traffic coordinator and once elected, the coordinator can establishan IVCP, as described above, and communicate it to the other vehiclesapproaching the potential-travel-priority-conflict zone. Optionally, thecoordinator can periodically re-broadcast its identity as trafficcoordinator and re-broadcast the IVCP to confirm control of thepotential-conflict zone and inform any newly arrived vehicles.

Once the traffic coordinator creates the IVCP and communicates tovehicles approaching a potential-travel-priority-conflict zone in theabove-described steps, the vehicles can then participate in the IVCP. Inone example, IVCP instructions are communicated to the vehiclesparticipating in the ad-hoc vehicle-based network corresponding to thepotential-travel-priority-conflict zone by providing each vehicle with avirtual traffic control to change speed, lateral/vertical profile of thevehicle to establish the IVCP.

In some embodiments, IVC systems can include mechanisms that allowcertain vehicles to have higher priority than other vehicles in havingthe right of way at intersections. This embodiment would, for example,facilitate and expedite the motion of priority vehicles through trafficin urban areas in the case of an emergency and/or in another typepriority situation. To enable such a priority scheme, one or more of twomechanisms may be utilized: detection of a priority vehicle when itapproaches and leaves an intersection and a priority assignment scheme.In some embodiments, prioritization may involve three or more levels ofpriority. For example, in one scheme, three priority levels areprovided: a highest priority for emergency vehicles en route to anemergency, an intermediate priority for mass-transit vehicles carryingmultiple passengers, and lowest priority for private passenger cars. Inthis example, the IVC system clears the route for the highest priorityvehicles as quickly and efficiently as possible, overriding any normalIVCP to create a high-priority IVCP. For intermediate-priority vehicles,the IVC system may weigh the travel directions and/or lanes containingmass-transit vehicles in a manner that allows each of those traveldirections and/or lanes to clear more quickly than they would if anon-priority vehicle were present in place of each mass-transit vehicle.

In order to allow for detection of a priority vehicle, upon approachinga travel-priority conflict zone, a priority vehicle may periodicallybroadcast a priority-request message to announce its presence and demandfor priority until it receives a priority-granted message from a trafficcoordinator.

The IVC system, such as an IVC system of an elected traffic coordinator,may receive a priority-request message from the priority vehicle, and,the IVC system may transmit a priority-granted message to the priorityvehicle. Priority-request messages and priority-granted messages maycontain substantially the same or similar information to a beaconingsignal, though they may additionally or alternatively contain anindication of the priority level of the priority vehicle (e.g.,emergency priority status, public transit priority status, funeralprocession priority status, etc.), travel-route information for thepriority vehicle, network identifiers for any current and/or pastpriority vehicles that have been granted priority and/or trafficcoordinators that have granted priority, and/or one or morepotential-conflict zone identifiers. Notably, in some embodiments, atraffic coordinator may additionally or alternatively detect thepresence of the priority vehicle by analyzing beaconing signalsoriginating from the priority vehicle, which may in some embodimentscontain any of the information that may otherwise be contained inpriority-request messages. After receiving a priority-granted message,the priority vehicle may be required to inform one or more othervehicles, such as a current traffic coordinator, of its departure from agiven potential-conflict zone via a priority-clear message so that anyvehicles proximate to the zone can resume standard IVCP operation.Priority-clear messages may contain substantially the same or similarinformation to a beaconing signal, though either may additionallyinclude a potential-conflict zone identifier. In order to provide apriority-clear message, when a priority vehicle exits or is within acertain time or distance of exiting a potential-conflict zone, it mayperiodically broadcast a priority-clear message for a period of time,which An IVC system on the priority vehicle may determine as a functionof the priority vehicle's location and/or velocity, the nature of thepotential-conflict zone, and/or other similar parameters. Ifpriority-clear messages do not reach the intended recipient(s), such asan elected traffic coordinator, the IVC system of the trafficcoordinator can deduce the departure of the priority vehicle bydetecting an absence of beaconing signals originating from the priorityvehicle for a certain period of time (i.e., a time-out period).

Tiered Ground Control and Air Traffic Control

In one aspect, an air control system includes a network of communicationtowers with ground control modules thereon; a traffic control computer;a plurality of air vehicles each providing flight plans with travelsegments in advance to the traffic control computer for approval,wherein the traffic control computer shares approved flight plans to theground control modules positioned in each travel segment for trackingthe vehicle and performing local air traffic control.

Implementations may include one or more of the following. The control ishierarchical, from national to state to city to city zones. The flightplan can include flight lanes as defined below. A flight plan conflictcontroller can arbitrate conflicting plans whose paths collide.

The ATC can include obstacle detection system for an air space includes:one or more air vehicles each having a plurality of environmentalsensors; a processor with a neural network in at least one vehicle or inat least one communication tower (edge processor) to receive sensor dataand identify the obstacle in the air space from sensor outputs.

FIG. 1I shows an exemplary ground based communication towers with airtraffic control (ATC) capability for the vehicles 10 using theelectronics of FIG. 1H. Together, the ATC 89 and flight control of thevehicles 10 can provide flight management, for example, separationassurance between vehicles 10; navigation assistance; weather andobstacle reporting; monitoring of speed, altitude, location, direction,etc.; traffic management; landing services; and real-time control.

The ATC 89 can be part of the flight control in the vehicle, and alsocan be external operated by the local ground control station (GCS), or acombination of internal and external ATC 89 for redundancy in coverage.The ATC 89 can be tiered where control can be done at the cell level,city level, state level, or national level, among others. One of thecellular towers can be the local ground control station communicablycoupled to the at least one vehicle. Specifically, the GCS may includecommunication means (such as a transceiver) to communicate with thevehicle via a network, such as radio network. Optionally, the networkmay be a bidirectional network to facilitate two-way communicationtherethrough. In another embodiment, the GCS may be a mobile device(such as a remote-control device) communicably coupled to the at leastone vehicle. According to an embodiment, the GCS may include equipmentsuch as processors, memory, display screens, and so forth.

In an embodiment, operation of the at least one vehicle may becontrolled completely autonomously using on-board computers. In anotherembodiment, operation of the at least one vehicle may be controlled atleast partially by the Ground Control Station. In such embodiment, ahuman operator at the GCS may operate the at least one vehicle. In theexternal ATC 89 GCS embodiment, the vehicle 10 flies from cell to celland receives navigation assistance/command from one of the cell towersdesignated as a controller. The controller communicates with one server92. In FIG. 1I, exemplary three cell towers provide associated cellcoverage areas for describing location determination of the vehicle 10.Typically, for a cell site, in rural locations, the coverage areas canbe about 5 miles in radius whereas, in urban locations, the coverageareas can be about 0.5 to 2 miles in radius. One aspect of the ATC 89 isto maintain a precise location at all time of the vehicles. This can beaccomplished in a plurality of ways, including a combination oftechniques such as triangulation based on the multiple cell towers,location identifiers from GPS/GLONASS transmitted over the cell networkfrom vehicles, sensors in the vehicle 10 for determining altitude,speed, etc., and the like.

Server 92 is distributed and shares information on each cell. Themaintained data can include current battery and/or fuel status for eachof the plurality of vehicles 10, and wherein the processing for thedelivery application authorization and management can include checkingthe current battery and/or fuel status to ensure the sufficiency toprovide a current delivery, for each of the plurality of vehicles 10.The maintained data can include photographs and/or video of a deliverylocation, and wherein the processing for the delivery applicationauthorization and management can include checking the delivery locationis clear for landing and/or dropping a package, for each of theplurality of vehicles 10. The maintained data can include photographsand/or video of a delivery location, and wherein the processing for thedelivery application authorization and management comprises, for each ofthe plurality of vehicles 10, checking the delivery location for adelivery technique including one of landing, dropping via a tether,dropping to a doorstep, dropping to a mailbox, dropping to a porch, anddropping to a garage. The plurality of vehicles 10 can be configured toconstrain flight based on coverage of the plurality of cell towers. Theconstrained flight can include one or more of pre-configuring theplurality of vehicles 10 to operate only where the coverage exists,monitoring cell signal strength by the plurality of vehicles 10 andadjusting flight based therein, and a combination thereof.

The server 92 can act as the ATC. One function performed by the ATC 89is separation assurance through altitude and flying lane coordination inaddition to the aforementioned air traffic control functions, packagedelivery authorization and management, landing authorization andmanagement, etc. As the ATC 89 has monitored data from various vehicles10, the ATC 89 can keep track of specific flight plans as well as causechanges in real time to ensure specific altitude and vector headings,i.e., a flight lane. For example, the ATC 89 can include a specificgeography of interest, and there can be adjacent ATCs that communicateto one another and share some overlap in the geography for handoffs. TheATC 89 can make assumptions on future flight behavior based on thecurrent data and then direct vehicles 10 based thereon. The ATC 89 canalso communicate with commercial aviation air traffic control systemsfor limited data exchange to ensure the vehicles 10 does not interferewith commercial aircraft or fly in no-fly zones. The server's method ofcommunicating flight data between a plurality of systems can includereceiving data indicative of flight objects. Flight information isextracted from the flight objects and rendered for viewing and editingalong with real time airspace environment data pertaining to the flightinformation. Modifications to the flight information are received andupdates to the flight objects are generated. Messages representative ofthe updated flight objects are generated that are compatible withsubscriber systems. The generated messages are communicated to thesubscriber systems across the one or more networks.

The systems and methods provide a hierarchical monitoring approach wherezones or geographic regions of coverage are aggregated into aconsolidated view for monitoring and control. The zones or geographicregions can provide local monitoring and control while the consolidatedview can provide national monitoring and control in addition to localmonitoring and control through a drill-down process. A consolidatedserver can aggregate data from various sources of control for zones orgeographic regions. From this consolidated server, monitoring andcontrol can be performed for any vehicle 10 communicatively coupled to awireless network.

In one embodiment, a national level server 92 runs code to: communicatewith a plurality of local servers each configured to communicate with aplurality of vehicles 10 in a geographic or zone coverage; consolidatedata from the plurality of local servers to provide a summary ofsuccessively larger geography having a plurality of geographic or zonecoverages; provide the summary data via a Graphical User Interface(GUI); and perform one or more functions via the GUI for air trafficcontrol and monitoring at an individual vehicle 10 level or group levelas desired.

The geographic boundary can be based on zip codes, county or townshipboundaries, geometric shapes, etc. The process 2300 can includecoordinating the data and analyzing between servers which manageadjacent regions. The process 2300 can include determining a pluralityof flying lanes including lanes which are fully within a singlegeographic region and lanes which traverse a plurality of geographicregions; and routing the one or more vehicles 10 in corresponding flyinglanes. The process can include handing off control of specific vehicles10 between servers based on transit in the lanes which traverse aplurality of geographic regions. The one or more vehicles 10 are routedto corresponding flying lanes to maximize collision-free trajectoriesbased on static obstructions, minimize travel time, and managecongestion in the geographic region. The process can include receivingflight data from the one or more vehicles 10; and updating air traffic,congestion, and obstructions based on the flight data. The one or morevehicles 10 each can include an antenna communicatively coupled to theone or more wireless networks, and wherein the flight is constrainedbased on the antenna monitoring cell signal strength during the flightand adjusting the flight based therein whenever the cell signal strengthis lost or degraded.

In a further embodiment, a drone air traffic control system includes aprocessor and a network interface communicatively coupled to oneanother; and memory storing instructions that, when executed, cause theprocessor to: communicate to one or more Unmanned Aerial Vehicles(vehicles 10) via one or more wireless networks to manage vehicle 10flight in a geographic region of a plurality of geographic regions,wherein the air traffic control system has one or more serversconfigured to manage each geographic region which is predetermined basedon a geographic boundary, wherein the one or more vehicles 10 areconfigured to maintain their flight in the plurality of geographicregions based on coverage or connectivity to the one or more wirelessnetworks; obtain data related to the one or more vehicles 10, whereinthe data includes flight operational data, flight plan data, and sensordata related to obstructions and other vehicles 10; analyze and storingthe data for each geographic region; and manage flight of the one ormore vehicles 10 in corresponding geographic regions based on the data.

Waypoint Management

In one aspect, a flight management system includes: a flight planningsystem that defines a trip as composed of one or more waypointsconnected by one or more flight lanes, and wherein each vehicle has aplurality of environmental sensors; a processor with a neural network inat least one vehicle or in at least one communication tower (edgeprocessor) to receive sensor data and to monitor vehicle passage throughplanned waypoint(s).

In another aspect, a flight management system for an air space includes:a map system that divides air space into layers of grids, each gridsupporting a lane of air travel for one or more air vehicles; aprocessor with a neural network in at least one vehicle or in at leastone communication tower (edge processor) to receive sensor data and tocenter the vehicle in the lane from sensor outputs, and further tonavigate all waypoints in a flight plan.

Implementations may include one or more of the following. A waypoint isa reference point in physical space used for purposes of navigation forvehicles 10. Waypoints on mapping programs provide a convenientmechanism to show location, start and end points, etc. The plurality ofwaypoints each includes a latitude and longitude coordinate defining apoint about which an area is defined for covering a portion of thegeographic region. The size of the area can be based on whether the areacovers an urban region, a suburban region, and a rural region in thegeographic area, wherein the size is smaller for the urban region thanfor the suburban region and the rural region, and wherein the size issmaller for the suburban region than for the rural region. Each of theplurality of waypoints can include an altitude range set based on flightaltitudes of the plurality of vehicles 10.

The waypoints can be used to provide operators and pilots visualinformation related to one or more vehicles 10. The waypoints can alsobe managed by the ATC 89 which uses one or more wireless networks and byassociated vehicles 10 in communication with the air traffic controlsystem. The waypoints can be defined based on the geography, e.g.,different sizes for dense urban areas, suburban metro areas, and ruralareas. The ATC 89 can maintain a status of each waypoint, e.g., clear,obstructed, or unknown. The status can be continually updated andmanaged with the vehicles 10 and used for routing the vehicles 10.

In an embodiment, the ATC uses a plurality of waypoints to manage airtraffic in a geographic region. Again, waypoints are sets of coordinatesthat identify a point in physical space. The waypoints can includelongitude and latitude as well as an altitude. For example, waypointscan be defined over some area, for example, a square, rectangle,hexagon, or some other geometric shape, covering some amount of area.The waypoints can cover a set area, such as every foot to hundred feetor some other distance. In an embodiment, waypoints can be set between1′ to 50′ in dense urban regions, between 1′ to 100′ in metropolitan orsuburban regions, and between 1′ to 1000′ in rural regions. Waypointscan also include an altitude. For UAV flights generally constrained toseveral hundred feet, the waypoints can either altitude or segment thealtitude in a similar manner as the area. For example, the altitude canbe separated in 100′ increments, etc. Accordingly, the defined waypointscan blanket an entire geographic region for management. The waypointscan be detected by the vehicles 10 using location identificationcomponents such as GPS. A typical GPS receiver can locate a waypointwith an accuracy of three meters or better when used with land-basedassisting technologies such as the Wide Area Augmentation System (WAAS).

The flight plan includes defining the flight paths based on specifyingtwo or more waypoints of the plurality of waypoints. A flight path canbe defined by one of specifying a start waypoint and an end waypoint andallowing a vehicle 10 to determine a path therebetween locally; andspecifying a start waypoint and an end waypoint and a plurality ofintermediate waypoints between the start waypoint and the end waypoint.The waypoint management method can score for the plurality of waypointsto determine the reliability and accuracy of the updates. The ATC 89 caninclude an obstruction database comprising a data structure for each ofthe plurality of waypoints defining a unique identifier of a locationand the obstruction status, and wherein the obstruction status comprisesone of clear, obstructed, and unknown. The waypoint management canupdate the obstruction status for each of the plurality of waypoints inthe obstruction database based on the received updates.

The ATC 89 and the vehicles 10 can use the waypoints for variouspurposes including i) flight path definition, ii) start and end pointdefinition, iii) tracking of vehicles 10 in flight, iv) measuring thereliability and accuracy of information from particular vehicles 10, v)visualizations of vehicle 10 flight, and the like. For flight pathdefinition, the waypoints can be a collection of points defining how aparticular vehicle 10 should fly. In an embodiment, the flight path canbe defined with waypoints across the entire flight path. In anotherembodiment, the flight path can be defined by various marker waypointsallowing the particular vehicle 10 the opportunity to determine flightpaths between the marker waypoints locally. In a further embodiment, theflight path is defined solely by the start and end waypoints, and thevehicle 10 locally determines the flight path based thereon.

The intermediate waypoints are monitored and used to manage the vehicle10 in flight. In an embodiment, the vehicle 10 can provide updates tothe ATC 89 based on obstruction detection as described herein. Theseupdates can be used to update the status of the waypoint directory inthe DB. The ATC 89 can use the waypoints as a mechanism to track thevehicles 10. This can include waypoint rules such as no vehicle 10 canbe in a certain proximity to another vehicle 10 based on the waypoints,speed, and direction. This can include proactive notifications based onthe current waypoint, speed, and direction, and the like.

In an embodiment, waypoints can be used for measuring the reliabilityand accuracy of information from particular vehicles 10. Again, thewaypoints provide a mechanism to define the geography. The Air trafficcontrol system 300 is configured to receive updates from vehicles 10about the waypoints. The ATC 89 can determine the reliability andaccuracy of the updates based on crowd-sourcing the updates.Specifically, the Air traffic control system 300 can receive an updatewhich either confirms the current status or changes the current status.For example, assume a waypoint is currently clear, and an update isprovided which says the waypoint is clear, then this vehicle 10providing the update is likely accurate. Conversely, assume a waypointis currently clear, and an update is provided which says the waypoint isnow obstructed, but a short time later, another update from anothervehicle 10 says the waypoint is clear, this may reflect inaccurateinformation. Based on comparisons between vehicles 10 and theirassociated waypoint updates, scoring can occur for the vehicles 10 todetermine reliability and accuracy. This is useful for the ATC 89 toimplement status update changes—preference may be given to vehicles 10with higher priority.

In another embodiment, an Air Traffic Control (ATC) system for AerialVehicles 10 includes a network interface and one or more processorscommunicatively coupled to one another, wherein the network interface iscommunicatively coupled to a plurality of vehicles 10 via one or morewireless networks; and memory storing instructions that, when executed,cause the one or more processors to communicate with a plurality ofvehicles 10 via one or more wireless networks comprising at least onecellular network; receive updates related to an obstruction status ofeach of a plurality of waypoints from the plurality of vehicles 10,wherein the plurality of waypoints are defined over a geographic regionunder control of the ATC 89 system; and manage flight paths, landing,and take-off of the plurality of vehicles 10 in the geographic regionbased on the obstruction status of each of the plurality of waypoints.

In a further embodiment, a non-transitory computer-readable mediumcomprising instructions that, when executed, cause one or moreprocessors to perform steps of communicating with a plurality ofvehicles 10 via one or more wireless networks comprising at least onecellular network; receiving updates related to an obstruction status ofeach of a plurality of waypoints from the plurality of vehicles 10,wherein the plurality of waypoints are defined over a geographic regionunder control of the ATC 89 system; and managing flight paths, landing,and take-off of the plurality of vehicles 10 in the geographic regionbased on the obstruction status of each of the plurality of waypoints.

Airway Management

In one aspect, an airway management system for an air space includes: amap system that divides air space into layers of grids, each gridsupporting an airway of air travel for one or more air vehicles eachhaving a plurality of environmental sensors; a processor with a neuralnetwork in at least one vehicle or in at least one communication tower(edge processor) to receive sensor data and to center the vehicle in theairway from sensor outputs.

Implementations may include one or more of the following. Each airwaycan be registered to landmarks on a physical map for recognition andcorrelation. For example, 3D airspace maps from 3Dairspace.org can beused. Google Earth is loaded on your computer, you only need todouble-click to launch the map inside Google Earth and see airspaceclassifications in 2D or 3D. The system can use the FAA's geoTIFF filesof its sectional charts. Inserting the geoTIFF into a CAD program canprovide the exact position position of a project on the sectional chartwith the FAA file containing the geoTIFF, an HTML help file, and a TFWpositioning file.

The vehicle 10 can use data from camera and lidar/radar to center itselfin the assigned airway. The airway is specified in 3D coordinates as aseries of vectors that the vehicle 10 can follow. Airways aregeographical paths for flight and are created, managed, and assigned bythe ATC 89. In an embodiment, the airway are based on Federal AviationAdministration (FAA) input, policies, and standards. The airway aredynamically managed and modified based on the FAA input, other airtraffic, weather, obstructions, and the like. The ATC 89 can beconfigured to route vehicles 10 to and from airway including based ondynamically changing airway, and to keep lateral separations betweenvehicles operating in the same airway or at the same altitude and in thesame proximity or geography, and with collision avoidance through ATC 89over wireless networks.

In another embodiment, multiple ATC 89 systems can manage vehicles 10over a geographic region with existing wireless networks providingconnectivity to the vehicles 10. For example, the boundaries can bebased on Zip code boundaries or some other existing boundary. Themultiple ATC 89 systems can manage vehicles 10 in their region based onthese boundaries, coordinate vehicle 10 traffic between regions, provideredundant coverage for adjacent regions, etc. Also, with the boundaries,the ATC 89 systems can develop, manage, and integrate airway with theboundaries.

For lateral separations between vehicles operating in the same airway orat the same altitude and in the same proximity or geography, thedistance between vehicles 10 is standardized and set based on thepurpose of a particular flying airway. For example, the airway may be anentry and exit airway allowing for vehicles 10 taking off to enter theATC 89, an intermediate airway that allows for some speed but also putsvehicles 10 in a position to move into an entry/exit airway, ahigh-speed airway (express) at a higher altitude allowing for vehicles10 to quickly reach their destination, and the like.

In an embodiment, standard distances between vehicles 10 may be closerin lower altitude/entry and exit airways where vehicle 10 speeds may belower than higher altitude airways. Standard distances between vehicles10 may be further in high altitude airways due to increased speed of thevehicles 10 and allow for more time for speed and course corrections andto avoid collisions.

The distance between vehicles 10 can be changed at any time and newinstructions sent to vehicles 10, from the ATC 89 via the wirelessnetworks 302, 304, to require speed changes or to hold position. The newinstructions can be based on changes in weather and more specificallystorms and rain, changes in wind speed and dealing with imprecise windspeed forecasts that impact drone speed and fuel usage (battery, gas),obstructions entering or expected to enter the flying airway(s), avehicle 10 experiencing a problem such as limited battery power or fuelleft, temporary flight restrictions that may include restrictedairspace, and the like.

The lateral separation accounts for vehicles 10 entering and leavingairway to account for the required takeoff, landing, and possiblehovering or delivery of products by vehicles 10 that must exit airway toachieve their objectives. All communications to and from vehicles 10occur over the wireless networks to and from the ATC 89 and/or backupATC centers. The airspeed for vehicles 10 can be measured and/orauthorized in knots and/or miles per hour (mph) within and outside ofthe airway to achieve appropriate lateral separations within the airway.The objective of these procedures is to ensure safe and efficient droneflights in the United States airspace.

The plurality of airways can include airways for entry and exit allowingthe one or more vehicles 10 to take off or land, airways forintermediate flight which are positioned adjacent to the airways forentry and exit, and airways for high speed at a higher altitude than theairways for intermediate flight. Distances between vehicles 10 can beset closer in the airways for entry and exit than in the airways forintermediate flight than in the airways for high speed. The newinstruction can be based on a change in weather comprising storms orrain. The new instruction can be based on a change in wind speed andbased on wind speed forecasts and associated impact on one or morevehicles 10 and their fuel usage. The new instruction can be based onobstructions entering or expected to enter the flying airway.

In another embodiment, an air traffic control system includes one ormore servers each comprising a network interface, a processor, andmemory; and a database communicatively coupled to the one or moreservers, wherein the network interface in each of the one or moreservers is communicatively coupled to one or more vehicles via aplurality of wireless networks at least one of which comprises acellular network, wherein a plurality of airway are defined andstandardized in the geographic region each based on a specific purpose,and wherein the one or more servers are configured to communicate to theone or more vehicles 10 over the one or more wireless networks;determine an associated airway of the plurality of airway for each ofthe one or more vehicles 10; communicate the associated airway to theone or more vehicles 10 over the one or more wireless networks; receivefeedback from the one or more vehicles 10 via one or more wirelessnetworks during flight in the associated flying airway; and provide anew instruction to the one or more vehicles 10 based on the feedback.

Navigation methods can receive an associated airway of the plurality ofairway from the air traffic control system over the one or more wirelessnetworks; provide feedback to the air traffic control system via the oneor more wireless networks during flight in the associated flying airway;receive a new instruction from the air traffic control system based onthe feedback; and implement the new instruction.

Flight Planning

In one aspect, a neural network is trained to generate flight plans fromhistorical data. In another aspect, a vehicle includes: one or more airvehicles each having a plurality of environmental sensors; a processorwith a neural network in at least one vehicle or in at least onecommunication tower (edge processor) to receive sensor data and follow apre-approved flight plan based from sensor outputs.

Implementations may include one or more of the following.

Flight planning is the process of producing a flight plan to describe aproposed aircraft flight. It involves two safety-critical aspects: fuelcalculation, to ensure that the aircraft can safely reach thedestination, and compliance with air traffic control requirements, tominimize the risk of midair collision. In addition, flight plannersnormally wish to minimize flight cost through appropriate choice ofroute, height, and speed, and by loading the minimum necessary fuel onboard. Air Traffic Services (ATS) use the completed flight plan forseparation of aircraft in air traffic management services, includingtracking and finding lost aircraft, during search and rescue (SAR)missions.

In one implementation, historical flight data, along with air trafficcontroller instructions are provided to a deep learning network, whichafter training, predicts the flight path based on history. The systempredicts flown route based on direct-to and heading instructions as thetraining data follow reality that controllers like to minimize thenumber of instructions to pilots. The future trajectory of the vehicleis modeled as a sequence of 4D coordinates that are correlated with itsrealized trajectory, last filed flight plan, which is a sequence of 2Dwaypoints, and weather conditions in the vicinity. One implementation isdesigned as a “sequence to sequence learning” problem, in which theinput sequence is the flight plan and the output is the actual flighttrajectory. The sequential learning problem is solved by anencoder-decoder recurrent neural network structure, where the encoderlearns from the flight plan and the decoder integrates the weatherinformation and recursively “translates” the embedded flight planinformation into a full 4D trajectory. Convolution layers can be usedinto the decoder network pipeline to extract representations from thehigh-dimension weather features.

A neural network examines flight data and is trained to generate one ormore flight plan for a single flight. The system can generate anelectronic plan for air traffic control and a plan for direct downloadinto an onboard flight management system. The flight planning system isto calculate how much energy (battery or gas) is needed in the airnavigation process by an aircraft when flying from an origin to adestination. The vehicle must also carry some reserve fuel to allow forunforeseen circumstances, such as an inaccurate weather forecast, or ATCtraffic control requiring an aircraft to fly at a lower-than-optimalaltitude due to congestion, or the addition of last-minute passengerswhose weight was not accounted for when the flight plan was prepared.There is often more than one possible route between two airports.Subject to safety requirements, costs are minimized by appropriatechoice of route, speed, and height.

Vehicles fly on airways under the direction of air traffic control. Anairway has no physical existence, but can be thought of as a motorway inthe sky. On an ordinary motorway, cars use different airways to avoidcollisions, while on an airway, aircraft fly at different flight levelsto avoid collisions. One can often see planes passing directly above orbelow one's own. Charts showing airways are published and are usuallyupdated every 4 weeks, coinciding with the AIRAC cycle. AIRAC(Aeronautical Information Regulation and Control) occurs every fourthThursday, when every country publishes its changes, which are usually toairways. Each airway starts and finishes at a waypoint, and may containsome intermediate waypoints as well. Waypoints use five letters (e.g.,PILOX), and those that double as non-directional beacons use three ortwo (TNN, WK). Airways may cross or join at a waypoint, so an aircraftcan change from one airway to another at such points. A complete routebetween airports often uses several airways. Where there is no suitableairway between two waypoints, and using airways would result in asomewhat roundabout route, air traffic control may allow a directwaypoint-to-waypoint routing, which does not use an airway (oftenabbreviated in flight plans as “DCT”). Most waypoints are classified ascompulsory reporting points; that is, the onboard flight managementsystem reports the aircraft's position to air traffic control as theaircraft passes a waypoint. Two main types of waypoints can be used: anamed waypoint appears on aviation charts with a known latitude andlongitude; and a geographic waypoint is a temporary position used in aflight plan, usually in an area where there are no named waypoints(e.g., most oceans in the Southern Hemisphere). The geographic waypointshave latitudes and longitudes that are a whole number of degrees.Complete routes are determined using airway(s) from origin todestination. Most flights over land fall into this category. Airway(s)from origin to an ocean edge, then an ocean track, then airway(s) fromocean edge to destination. Most flights over northern oceans fall intothis category. Airway(s) from origin to an ocean edge, then afree-flight area across an ocean, then airway(s) from ocean edge todestination. Most flights over southern oceans fall into this category.Free-flight area from origin to destination can be done, air trafficcontrol still requires a position report about once an hour. Flightplanning systems organize this by inserting geographic waypoints atsuitable intervals. The particular route to be flown determines theground distance to cover, while winds on that route determine the airdistance to be flown. Each inter-waypoint portion of an airway may havedifferent rules as to which flight levels may be used. Total aircraftweight at any point determines the highest flight level which can beused. Cruising at a higher flight level generally requires less fuelthan at a lower flight level, but extra climb fuel may be needed to getup to the higher flight level (it is this extra climb fuel and thedifferent fuel consumption rate that cause discontinuities). The neuralnetwork determines a least-cost flight based only on time; fuel,fuel/time, or fuel costs and time costs and overflight charges. Despiteall the effort taken to optimize flight plans, there are certaincircumstances in which it is advantageous to file suboptimal plans. Inbusy airspace with a number of competing vehicles, the optimum routesand preferred altitudes may be oversubscribed. This problem can be worsein busy periods, such as when everyone wants to arrive as soon as itopens for the day. If all the aircraft file optimal flight plans then toavoid overloading, air traffic control may refuse permission for some ofthe flight plans or delay the allocated takeoff slots. To avoid this asuboptimal flight plan can be filed, asking for an inefficiently lowaltitude or a longer, less congested route.

The system automatically generates flight plans, secondary, or alternateflight plans, where the generated flight plans are free of obstacles ordiscontinuities. If obstacles, weather, or other vehicle's plansconflict with the present plan (discontinuity) exist, thediscontinuities are automatically removed and a discontinuity-freeflight plan is generated. In an example, if a discontinuity isidentified the efficiency and operational flight object system isconfigured to perform the following steps: 1. The discontinuity isidentified in the flight plan; 2. Limitations are identified applicableto the end user's system and the source; 3. A navigational database isaccessed to determine known waypoints that can be used to remove thediscontinuity; 4. Create unique waypoint and maneuver instructionsspecific to each vehicle; and 5. Determine real time operationalrestrictions, and user preference, to generate specific communicationsprotocols to invoke a flight information message free fromdiscontinuities for the user.

Additionally, a revision of a flight plan includes deleting or addingwaypoints, modifying the position of waypoints, or modifying thecharacteristics pertaining to the waypoints or legs between waypoints,such as the manner in which the aircraft maneuvers, aircraft speed, timeof arrival at the waypoint, or altitude. The characteristics for variouswaypoints or legs, segments joined by waypoints or fixes, furtherexamples include weather bands. A weather band is a collection ofenvironmental information for a specific or series of spatial points,such as a specific altitude or a series of three- or four-dimensionalpoints in space and time. Starting from a line from origin todestination, the deviation includes flying over, under, or around theobstacles in 3D space and time (the 4^(th) dimension).

Once approved, the system can generate graphical depictions such as adepiction of a lateral profile of a flight plan, a vertical profile of aflight plan, and a speed profile associated with the lateral portion ofthe flight plan. FIG. 1K shows examples of graphical depictions of anactive flight plan and actual flight information in conjunction withmultiple flight plans, flight histories, and real time flightinformation. The UI highlights or annunciates specific flightinformation history such as past flight plans specific to that aircraftor flight, or flight information from any flight may be applied forcomparison. Any flight, and its flight information, may be used forcomparison as long as at least one flight information parameter can becorrelated to the current flight selection. The correlation parameterscan be manually selected or automated. Automation is the preferredmethod to detect the flights and flight information that is of closetmatch. For example, the options can be configured by similar flightroute, portion of a flight route, speeds, altitude, aircraft type, daterange, origin, destination, departure time, arrival time, tail number,pilot's name, or flight number. In one illustrative example, a flightplan includes an estimated time to reach a waypoint. When the aircraftactually crosses the waypoint, the event is captured by cell towers onthe ground to determine the actual crossing time and send a messageincluding the actual crossing time to the server. The actual crossingtime can be displayed and recorded automatically on the user's computingdevice by mobile application, and an update to the original flight planis generated and made available for viewing on the user's computingdevice.

An authorized user can dynamically make changes to a flight plan andcommunicate the changes across multiple or local systems andsubscribers. The changes are synchronized across the multiple or localsystems. In order to accomplish this synchronization, messages areautomatically generated for each of the systems' and subscriber'scommunication protocols. The systems and subscribers include theon-board flight management system, mobile devices, local agencies, andATC. The changes, their status, and associated information can be viewedin real-time. By providing a way to update flight plans fromheterogeneous systems, dynamic updates to flight plans from varioussources can be accommodated in an efficient manner.

An approved user (pilot, dispatcher, air traffic controller) can view agraphical depiction of an active flight plan in conjunction withmultiple flight plans and flight histories. In one embodiment, specificflight history data, past flight plan, or flight history most related tothe active flight plan is highlighted or annunciated. Various optionsare configurable by the user. For example, options can be configured bysimilar route, speeds, altitude, aircraft type, date range, origin,destination, departure time, arrival time, tail number, pilot's name, orflight number of one or more airline operators. In one embodiment, alldata stored in the flight history database are searched, and the flightsor flight data most analogous to the active flight plan are identified.

In one implementation, the ATC 89 is compatible with NASA's UTM systemto enable safe and efficient low-altitude airspace operations byproviding services such as airspace design, corridors, dynamicgeofencing, severe weather and wind avoidance, congestion management,terrain avoidance, route planning and re-routing, separation management,sequencing and spacing, and contingency management. UTM could provide tohuman managers the data to make strategic decisions related toinitiation, continuation, and termination of airspace operations toensure that only authenticated vehicle could operate in the airspace. Inits most mature form, the UTM system could be developed using autonomycharacteristics that include self-configuration, self-optimization andself-protection. The self-configuration aspect could determine whetherthe operations should continue given the current and/or predictedwind/weather conditions.

One embodiment is a Portable ATC 89 system, which would move frombetween geographical areas and support operations such as precisionagriculture and disaster relief. The second type of system would be aPersistent ATC 89 system, which would support low-altitude operationsand provide continuous coverage for a geographical area. Either systemwould require persistent communication, navigation, and surveillance(CNS) coverage to track, ensure, and monitor conformance.

Flight plans may be used to document basic information such as departureand arrival points, estimated time en route, various waypoints that theaircraft must traverse enroute, information pertaining to thosewaypoints, such as actual or estimated altitude and speed of theaircraft at those waypoints, information relating to legs of the flightbetween those waypoints, and aircraft predicted performance. This typeof flight plan may be used to construct a flight trajectory includingthe various legs of the flight, which are connected to the variouswaypoints along the route. Flight plans may be used to construct aflight trajectory including the various legs of the flight which areconnected to various waypoints along the route. The flight trajectorymay include a lateral trajectory defined in the horizontal plane and avertical trajectory defined in the vertical plane. The flight trajectorymay also include the element of time across the horizontal and verticalplanes. Flight intent information generally refers to the future flighttrajectory of an aircraft expressed as a four-dimensional profile untildestination. Flight prediction information also relates to the futureflight trajectory, however it is generally limited to a pilot'sperspective of information pertinent to the flight. Flight intentinformation may contain additional flight parameters required by groundsystems.

Flight Approval

In one aspect, a neural network is trained to generate flight plans fromhistorical data, and such generated plans can be used to approve theflight based on cost factors, among others. In another aspect, a vehicleincludes: one or more air vehicles each having a plurality ofenvironmental sensors; a processor with a neural network in at least onevehicle or in at least one communication tower (edge processor) toreceive sensor data and follow a pre-approved flight plan based fromsensor outputs.

Implementations may include one or more of the following.

Ground systems would use the additional information to perform functionssuch as the issuance of speed or time clearances. FIG. 1J shows anexemplary flight approval process to the ATC 89 which includes:

Start with a direct line between origin and destination and identifywaypoints (110)

Look up known obstacles and deviate around obstacles (112)

Look up weather issues and deviate around problematic weather (114)

Look up conflicting filed flight plans and deviate around conflicts withother vehicles (116)

Submit flight plan with origin and destination to approval service (118)

If rejected, provide revised plan and resubmit (120)

During flight, if new obstacles are detected, deviate around newobstacle and update the ATC 89 with new info on the new obstacle (122).

Combining Flight Plan with Autonomous Flight Improves Safety

In one aspect, an air control system includes a network of communicationtowers with ground control modules thereon; a traffic control computer;a plurality of air vehicles each providing flight plans with travelsegments in advance to the traffic control computer for approval,wherein the traffic control computer shares approved flight plans to theground control modules positioned in each travel segment for trackingthe vehicle and performing local air traffic control, and wherein eachvehicle operates at a reduced power mode based on the approved planuntil an unexpected obstacle is encountered, where additional sensorsare powered on to help the vehicle navigate. The additional sensors canbe on the communication towers to save cost in case of package deliverydrones, where cost considerations outweigh the need for absoluteperfection in anti collision (such as those for humans).

Implementations may include one or more of the following. Thepre-approved flight plan reduces the surprises that may pop up duringthe flight, requiring fewer resources for continuous autonomous flightnavigation which consumes power. Further, the pre-cleared flight planmeans that other autonomous vehicles should not interfere with thepresent vehicle's travel, absent some abnormalities, in which case theautonomous system takes over to avoid the obstacle. Safety is improved.Safety is further enhanced when vehicles travel as a group. Further,fuel efficiency is improved as the lift is improved for all members ofthe flock traveling together.

Flock Travel

In one aspect, flight vehicles can travel as a flock of birds when theirflight plans have at least one spatially and temporally common travelsegment. Each vehicle runs a neural network trained to follow the othervehicles as a flock, and wherein a traffic control computer sharesapproved flight plans to the ground control modules positioned in eachtravel segment for tracking the vehicle and performing local air trafficcontrol for the flock.

Implementations may include one or more of the following. Next a flockcontrol behavior is detailed. Quite often users are going to a commondestination. For example, a group of planes may want to go from point Ato point B. A plurality of vehicles follow a leader case, who in turn isfollowing a target vehicle or a target driving plan. The leader, or thefirst car in the group would automatically or manually take evasiveactions to avoid an obstacle, and the information is transmitted viavehicle to vehicle communication such as Bluetooth, Wifi or even DSRC tofollowing vehicles, and the driving path of the entire flock is adjustedaccording to the obstacle. “Flocking” is the collective motion of alarge number of self-propelled entities and is a collective animalbehavior exhibited by many living beings such as birds, fish, bacteria,and insects. It is considered an emergent behavior arising from simplerules that are followed by individuals and does not involve any centralcoordination. The vehicle communications would identify vehiclestraveling as a flock, and the vehicles perform distributed flockingoperation by communication over the wireless network.

The Vehicles Rules are discussed next. One embodiment simulates simpleagents (vehicles) that are allowed to move according to a set of basicrules. The result is akin to a flock of birds, a school of fish, or aswarm of insects. In one embodiment, flocking behavior for each vehicleis controlled by three rules:

Separation—avoid crowding neighbors (short range repulsion)

Alignment—steer towards average heading of neighbors

Cohesion—steer towards average position of neighbors (long rangeattraction)

Rule 1: Vehicles try to go towards the center of mass of neighboringvehicles. The ‘center of mass’ is simply the average position of all thevehicles. Assume there are N vehicles, called b1, b2, . . . , bN. Also,the position of a vehicle b is denoted b.position. Then the ‘center ofmass’ c of all N vehicles is given by:c=(b1.position+b2.position++bN.position)/N

However, the ‘center of mass’ is a property of the entire flock ofvehicles; it is not something that would be considered by an individualvehicle. Each vehicle is moved toward its ‘perceived center’, which isthe center of all the other vehicles, not including itself. Thus, forvehicleJ (1<=J<=N), the perceived center pcJ is given by:

pcJ=(b1.position+b2.position+ . . . +bJ−1.position+bJ+1.position+ . . .+bN.position)/(N−1)

Having calculated the perceived center, the system moves the vehicletowards it. To move it 1% of the way towards the center this is given by(pcJ−bJ.position)/100 as:

PROCEDURE rule1(vehicle bJ)  Vector pcJ  FOR EACH VEHICLE b   IF b != bJTHEN pcJ = pcJ + b.position  pcJ = pcJ / N−1  RETURN (pcJ − bJ.position)/ 100

Rule 2: Vehicles try to keep a small distance away from other objects(including other vehicles). The rule ensures vehicles don't collide intoeach other. If each vehicle within a defined small distance (say 100units) of another vehicle, the vehicle is moved away. This is done bysubtracting from a vector c the displacement of each vehicle which isnear by.

PROCEDURE rule2(vehicle bJ)  Vector c = 0;  FOR EACH VEHICLE b  IF b !=bJ THEN   IF |b.position − bJ.position| < 100 THEN c = c − (b.position −  bJ.position)  RETURN c

If two vehicles are near each other, they will be slightly steered awayfrom each other, and at the next time step if they are still near eachother they will be pushed further apart. Hence, the resultant repulsiontakes the form of a smooth acceleration. If two vehicles are very closeto each other it's probably because they have been driving very quicklytowards each other, considering that their previous motion has also beenrestrained by this rule. Suddenly jerking them away from each other isnot comfortable for passengers and instead, the processes have them slowdown and accelerate away from each other until they are far enoughapart.

Rule 3: Vehicles try to match velocity with near vehicles.

This is similar to Rule 1, however instead of averaging the positions ofthe other vehicles we average the velocities. We calculate a ‘perceivedvelocity’, pvJ, then add a small portion (about an eighth) to thevehicle's current velocity.

PROCEDURE rule3(vehicle bJ)  Vector pvJ  FOR EACH VEHICLE b   IF b != bJTHEN    pvJ = pvJ + b.velocity   END IF  END  pvJ = pvJ / N−1  RETURN(pvJ − bJ.velocity) / 8 END PROCEDURE

Additional rules is implemented as a new procedure returning a vector tobe added to a vehicle's velocity.

Action of a crowd or traffic is discussed next. For example, to handlestrong traffic.

PROCEDURE strong_traffic(Vehicle b)  Vector traffic  RETURN traffic ENDPROCEDURE

This function returns the same value independent of the vehicle beingexamined; hence the entire flock will have the same push due to thetraffic or crowd. Limiting the speed of vehicles is discussed next. Fora limiting speed vlim:

PROCEDURE limit_velocity(Vehicle b) Integer vlim Vector v IF|b.velocity| > vlim THEN b.velocity = (b.velocity / |b.velocity|) * vlimEND IF END PROCEDURE

This procedure creates a unit vector by dividing b.velocity by itsmagnitude, then multiplies this unit vector by vlim. The resultingvelocity vector has the same direction as the original velocity but withmagnitude vlim.

The procedure operates directly on b.velocity, rather than returning anoffset vector. It is not used like the other rules; rather, thisprocedure is called after all the other rules have been applied andbefore calculating the new position, ie. within the proceduremove_all_vehicles_to_new_positions:

b.velocity=b.velocity+v1+v2+v3+ . . .

limit_velocity(b)

b.position=b.position+b.velocity

Bounding the position is discussed next. In order to keep the flockwithin a certain zone so that they can drive out of them, but thenslowly turn back, avoiding any harsh motions.

PROCEDURE bound_position(Vehicle b)  Integer Xmin, Xmax, Ymin, Ymax,Zmin, Zmax  Vector v  IF b.position.x < Xmin THENv.x = 10   ELSE IFb.position.x > Xmax THENv.x = −10  IF b.position.y < Ymin THENv.y = 10  ELSE IF b.position.y > Ymax THENv.y = −10  IF b.position.z < ZminTHENv.z = 10   ELSE IF b.position.z > Zmax THENv.z = −10  RETURN v

Here of course the value 10 is an arbitrary amount to encourage them todrive in a particular direction.

During the course of flock control, one may want to break up the flockfor various reasons. For example the introduction of a predator maycause the flock to scatter in all directions. The predator can be anobject on an impending collision course with the flock. Scattering theflock can be done. Here the flock can disperse; they are not necessarilymoving away from any particular object, but to break the cohesion (forexample, the flock encounters a dangerously driven vehicle). Thus thesystem negates part of the influence of the vehicles rules.

PROCEDURE move_all_vehicles_to_new_positions( )

FOR EACH VEHICLE b

v1=m1*rule1(b)

v2=m2*rule2(b)

v3=m3*rule3(b)

b.velocity=b.velocity+v1+v2+v3+ . . .

b.position=b.position+b.velocity

When the risk of collision arises, the process can make m1 negative toscatter the flock. Setting m1 to a positive value again will cause theflock to spontaneously re-form.

Tendency away from a particular place is handled next. If the flock isto continue the flocking behavior but to move away from a particularplace or object (such as a car that appears to collide with the flock),then we need to move each vehicle individually away from that point. Thecalculation required is identical to that of moving towards a particularplace, implemented above as tend_to_place; all that is required is anegative multiplier: v=−m*tend_to_place(b).

The vehicles can be organized into a V formation (sometimes called askein) is the symmetric V-shaped formation for Drag Reduction and FuelSaving where all the cars except the first drive in the upwash from thewingtip vortices of the car ahead. The upwash assists each car insupporting its own weight in flight, in the same way a glider can climbor maintain height indefinitely in rising air.

The flying vehicles of the flock establish a target vehicle a referencefor flocking. The leading flying vehicle of the flock is established asthe target flying vehicle by the flying vehicles of the flock. Thetarget flying vehicle may be established before the flying vehicle startrunning in flock. In another embodiment, the first flying vehicle of theflock detects a preceding flying vehicle with the information from theradar or the camera on the leading flying vehicle or flock leader, andautomatically establishes the detected preceding flying vehicle as a newtarget flying vehicle. By successively changing new target flyingvehicles in this manner, new flying vehicles may automatically be addedto the flock. Even if a flying vehicle is incapable of communicationbetween flying vehicles, that flying vehicle may be established as atarget flying vehicle according to an algorithm described later on.

In one embodiment, the leading flying vehicle of the flock establishes ahypothetical target flying vehicle, and transmits items of informationof the hypothetical target flying vehicle to the other flying vehiclesof the flock which follow the flock leader through camera visualtracking, Bluetooth or WiFi, among others.

Each vehicle in the flock is responsible for generating a speed planwhich governs the relationship between the position in which the flyingvehicle runs and the speed at which the flying vehicle runs. Thevehicles perform determining, based on the speed plan, a plannedposition to be reached from the present position of the flying vehicleafter a predetermined time t, e.g., 1.5 seconds, and a planned speed ofthe flying vehicle at the planned position in the flock. According tothis function, if the speed plan from the present position of the flyingvehicle is generated such that the flying vehicle is to maintain thespeed of 80 km/h, i.e., 22.2 m/sec., then the planned position to bereached after the predetermined time t, e.g., 1.5 seconds, is 33.3 mspaced from the present position down the running path B, and theplanned speed at the planned position to be reached is 80 km/h.

The function as the predicted value calculating means serves todetermine a predicted position and a predicted speed to be reached bythe flying vehicle after the predetermined time t. The predictedposition is calculated from the present position, i.e., the traveleddistance, the present speed, and the present acceleration of the flyingvehicle which are given from the communication module 1, and thepredicted speed is calculated from the present speed and the presentacceleration of the flying vehicle.

The speed/acceleration of the vehicle, based on which the predictedposition and the predicted speed will be determined, is basicallydetermined from the speedometer. The predicted position and thepredicted speed are determined using the speed and the acceleration ofthe flying vehicle and GPS position.

A distance deviation, i.e., a position error, between a planned positionto be reached by the flying vehicle after the predetermined time t basedon the speed plan and the predicted position, described above, to bereached by the flying vehicle, and a speed deviation, i.e., a speederror, between a planned speed to be reached by the flying vehicle afterthe predetermined time t based on the speed plan and the predictedspeed, described above, to be reached by the flying vehicle aredetermined. These deviations are calculated by subtracting.

The target flying vehicle may be a flock leader. If, however, the targetflying vehicle is not a flock leader, then the flock leader calculates aposition, a speed, and an acceleration of the target flying vehicleusing the laser radar, GPS, or triangulation of RF signals, for example.

Based on the above control algorithm, the engine throttle valve opening,the transmission, and the brake of each of plural following flyingvehicles are controlled to control the flying vehicles in a flock.

The system detects the positional data of the preceding flying vehiclethrough inter-vehicular communications or the laser radar, and controlsthe following flying vehicle in the event that the preceding flyingvehicle drops out of a normal control range of the vehicle flockcontrol. Even when a flying vehicle drops out of the normal range of thevehicle flock control, the control algorithm controls a following flyingvehicle to increase its inter-vehicular distance up to such a flyingvehicle. Therefore, the vehicle platoon control will not be interruptedeven when one or more flying vehicles drops out of the platoon.

If it is known that a group of flying vehicles will travel in platoon orflying vehicles are counted at a tollgate or the like and theincremental count is indicated to each flying vehicle to let itrecognize its position in the platoon, then it is possible to establishthe position i for each of the flying vehicles before they travel inplatoon.

However, in order to handle a situation where another flying vehiclepulls in between flying vehicles running in platoon or another flyingvehicle is added to a front or rear end of a platoon of flying vehicles,the process according to the present system makes it possible for eachof the flying vehicles running in flock to recognize its positionrelative to a target flying vehicle through inter-vehicularcommunications.

There are two procedures available for each of the flying vehiclesrunning in flock to recognize its position relative to a target flyingvehicle. The first procedure is applicable to local inter-vehicularcommunications by which each of the flying vehicles of the flock cancommunicate with only those flying vehicles which run immediately infront of and behind the flying vehicle. If the flock leader of a flockis selected as a target flying vehicle, then the target flying vehicletransmits its own positional information i=0 to a next flying vehiclewhich immediately follows the target flying vehicle. The followingflying vehicle adds 1 to i, producing its own positional informationi=1, recognizes that it is the second flying vehicle from the targetflying vehicle, and transmits its own positional information i=1 to anext flying vehicle which immediately follows the second flying vehicle.Having received the positional information i=1, the next immediatelyfollowing flying vehicle adds 1 to i, producing its own positionalinformation i=2, recognizes that it is the third flying vehicle from thetarget flying vehicle, and transmits its own positional information i=2to a next flying vehicle which immediately follows the third flyingvehicle. In this manner, each of the flying vehicles is able torecognize its position relative to the target flying vehicle with ameans for counting its position and local inter-vehicularcommunications.

If a target flying vehicle is not the flock leader of a flock and thetarget flying vehicle and the flock leader cannot communicate with eachother through inter-vehicular communications, then the flock leader setsits own positional information to i=1, and transmits the own positionalinformation i=1 to a next flying vehicle which immediately follows thetarget flying vehicle.

According to the present system, as described above, a longitudinalacceleration correcting quantity of each of the flying vehicles of aflock is determined on the basis of predicted deviations of a positionand a speed that are predicted after a predetermined time, from a speedplan, and the speed of the flying vehicle is controlled on the basis ofthe determined longitudinal acceleration correcting quantity. Therefore,the flying vehicles can smoothly be controlled to run in flock along arunning path on a road.

A longitudinal acceleration correcting quantity of a flying vehiclefollowing a target flying vehicle is determined on the basis of aninter-vehicular distance between the following flying vehicle and thetarget flying vehicle and a speed difference there-between after apredetermined time, and the speed of the following flying vehicle iscontrolled on the basis of the determined longitudinal accelerationcorrecting quantity. Consequently, the following flying vehicle canautomatically be driven smoothly along a running path on a road whilereliably keeping a proper inter-vehicular distance between the followingflying vehicle and the target flying vehicle.

Since the system arrangements on a flock leader and a following flyingvehicle of a flock are identical to each other, the flock leader and thefollowing flying vehicle can automatically be driven in a manner tomatch them using slightly different software or program adaptations madetherefor. Therefore, any one of the flying vehicles of the flock maybecome a flock reader or a following flying vehicle.

Each of following flying vehicles of a flock is not only controlled withrespect to a flock leader, but also always monitors an inter-vehiculardistance between itself and a preceding flying vehicle, so that it canincrease the inter-vehicular distance even when a flying vehicle dropsout of the flock. Therefore, it is not necessary to stop controlling thevehicle flock control when a flying vehicle drops out of the flock. Evenwhen a flying vehicle drops out of a flock, the vehicle flock controlsystem does not stop controlling the other flying vehicles to run inflock, and when the flying vehicle that has dropped out returns to theflock, the vehicle flock control system can continuously control theflying vehicles to run in flock. The vehicle flock control system allowsdifferent types of flying vehicles, such as vehicles of differentlengths, smaller automobiles, larger automobiles, etc., to be mixed in aflock, and can control those flying vehicles to run in flock.Accordingly, the vehicle flock control system according to the presentsystem is capable of stably controlling flying vehicles to run in flockon a road designed for flying vehicles to run automatically, andparticularly of controlling the speeds of such flying vehicles smoothly.

In some embodiments, a lead vehicle identifies airway information thatmay include airway markings on the ground surface, and the computersystem may use one or more sensors to sense the airway markings Forexample, the computer system may use an image-capture device to captureimages of the road and may detect the airway markings by analyzing theimages for predetermined colors, shapes, and/or brightness levels thatare similar to a predetermined color, shape, and/or brightness of theairway markings. As another example, the computer system may project alaser onto the road and may detect the airway markings by analyzingreflections off the road for an intensity that is similar to apredetermined intensity of a reflection off the airway markings. Thecomputer system may estimate the location of the airway based on thesensed airway markings and control the vehicle to follow the airway. Thevehicles behind the lead vehicle can then simply follow the lead vehicleas part of a flock.

At some point, the lead vehicle may determine that the airwayinformation has become unavailable or unreliable. For example, severefog may be present and severely affect the airway markings. In otherexamples, the vehicle may no longer be able to detect the airwaymarkings on the road, the vehicle may detect contradictory airwaymarkings on the road, the vehicle may no longer be able to determine thegeographic location of the vehicle, and/or the vehicle may not be ableto access a predetermined map of the road. Other examples are possibleas well. In response to determining that the airway information hasbecome unavailable or unreliable, the computer system may use at leastone sensor to monitor at least one neighboring vehicle, such as aneighboring vehicle in a neighboring airway or a neighboring vehiclebehind the vehicle that is part of the flock. The computer system maythen control the vehicle to maintain a distance between the vehicle andthe at least one neighboring vehicle to be at least a predeterminedminimum distance and even if the vehicle is unable to rely on the airwayinformation to estimate the location of the airway on the road, thevehicle may avoid colliding with the at least one neighboring vehicle.

In other embodiments, the airway information may include a geographiclocation of the vehicle and a predetermined map of the road. Thecomputer system may determine the geographic location of the vehicle by,for example, querying a location server for the geographic location ofthe vehicle. Alternatively, if the predetermined map indicates ageographic location of at least two objects near the vehicle, thecomputer system may determine the geographic location of the vehicle by,for example, using a laser rangefinder or light detection and ranging(LIDAR) unit to estimate a distance from the vehicle to the at least twoobjects near the vehicle and determining the geographic location of thevehicle using triangulation. Other examples are possible as well. In anycase, the computer system may then locate the geographic location of thevehicle on the predetermined map to determine the location of the airwayrelative to the geographic location of the vehicle.

In still other embodiments, the airway information may be derived from aleading vehicle that is in front of the vehicle in the airway andcorrelation with other information such as map data and independentairway analysis to prevent the blind-following-the blind situation. Thecomputer system may estimate a path of the leading vehicle using, forexample, a laser rangefinder and/or a LIDAR unit. Other examples arepossible as well. Once the computer system has estimated the path of theleading vehicle, the computer system may estimate the location of theairway based on the estimated path. For example, the computer system mayestimate the location of the airway to include the estimated path (e.g.,extend by half of a predetermined airway width on either side of theestimated path). Other examples are possible as well.

In some embodiments, the computer system may maintain a predeterminedthreshold for the airway information, and the computer system maydetermine that the airway information has become unavailable orunreliable when the computer system detects that a confidence of theairway information (e.g., how confident the computer system is that theairway information is reliable) is below the predetermined threshold. Insome embodiments, the computer system may additionally maintain apredetermined time period for the airway information, and the computersystem may determine that the airway information has become unavailableor unreliable when the computer system detects that a confidence of theairway information is below the predetermined threshold for at least apredetermined amount of time.

Upon determining that the airway information has become unavailable orunreliable, the computer system may use at least one sensor to monitorat least one neighboring vehicle. The at least one neighboring vehiclemay include, for example, a neighboring vehicle in an airway adjacent tothe airway in which the vehicle is traveling. As another example, the atleast one neighboring vehicle may include a neighboring vehicle behindthe vehicle in the airway in which the vehicle is traveling. As stillanother example, the at least one neighboring vehicle may include afirst neighboring vehicle and a second neighboring vehicle, each ofwhich may be either in an airway adjacent to the airway in which thevehicle is traveling or behind the vehicle in the airway in which thevehicle is traveling. Other examples are possible as well.

When the airway information has become unavailable or unreliable, thecomputer system may control the vehicle to maintain a distance betweenthe vehicle and the at least one neighboring vehicle to be at least apredetermined distance. The predetermined distance may be, for example,a distance determined to be a safe distance and/or a distanceapproximately equal to the difference between a predetermined airwaywidth and a width of the vehicle. Other predetermined distances arepossible as well.

In order to maintain the distance between the vehicle and the at leastone neighboring vehicle to be at least the predetermined distance, thecomputer system may continuously or periodically use the at least onesensor on the vehicle to monitor the distance between the vehicle andthe at least one neighboring vehicle. The computer system may monitorthe distance between the vehicle and the at least one neighboringvehicle using, for example, a laser rangefinder and/or LIDAR unit. Ifthe distance between the vehicle and the at least one neighboringvehicle becomes less than the predetermined distance, the computersystem may move the vehicle away from the at least one neighboringvehicle in order to maintain the distance between the vehicle and the atleast one neighboring vehicle to be at least the predetermined distance.

In some embodiments, in addition to maintaining the distance between thevehicle and the at least one neighboring vehicle to be at least thepredetermined distance, the computer system may additionally maintainthe distance between the vehicle and the at least one neighboringvehicle to be within a predetermined range of the predetermineddistance. In these embodiments, if the distance between the vehicle andthe at least one neighboring vehicle becomes too large (e.g., no longerwithin the predetermined range of the predetermined distance), thecomputer system may move the vehicle closer to the at least oneneighboring vehicle. This may, for example, prevent the vehicle fromdrifting too far away from the neighboring vehicle that the vehicledrifts into an airway on the opposite side of the vehicle from theneighboring vehicle.

As noted above, in some embodiments the at least one vehicle may includea first neighboring vehicle and a second neighboring vehicle. In theseembodiments, maintaining the distance between the vehicle and the atleast one neighboring vehicle may involve maximizing both a firstdistance between the vehicle and the first neighboring vehicle and asecond distance between the vehicle and the second neighboring vehicle(e.g., such that the vehicle remains approximately in the middle betweenthe first neighboring vehicle and the second neighboring vehicle). Eachof the first distance and the second distance may be at least thepredetermined distance.

In some embodiments, in addition to maintaining the distance between thevehicle and the at least one neighboring vehicle to be at least thepredetermined distance, the computer system may determine an updatedestimated location of the airway. To this end, the computer system mayuse the at least one sensor to monitor at least a first distance to theat least one neighboring vehicle and a second distance to the at leastone vehicle. Based on the first distance and the second distance, thecomputer system may determine a first relative position and a secondrelative position (e.g., relative to the vehicle) of the at least oneneighboring vehicle. Based on the first relative position and the secondrelative position, the computer system may estimate a path for the atleast one neighboring vehicle. The computer system may then use theestimated path to determine an updated estimated location of the airway.For example, in embodiments where the at least one neighboring vehicleis traveling in an airway adjacent to the airway in which the vehicle istraveling, the computer system may determine the estimated location ofthe airway to be substantially parallel to the estimated path (e.g., theairway may be centered on a path that is shifted from the estimated pathby, e.g., a predetermined airway width and may extend by half of thepredetermined airway width on either side of the path). As anotherexample, in embodiments where the at least one neighboring vehicle istraveling behind the vehicle in the airway in which the vehicle istraveling, the computer system may determine the estimated location ofthe airway to be an extrapolation (e.g., with constant curvature) of theestimated path. Other examples are possible as well.

In some embodiments, the computer system may additionally use a speedsensor to monitor a speed of the at least one neighboring vehicle andmay modify a speed of the vehicle to be less than the speed of the atleast one neighboring vehicle. This may allow the vehicle to be passedby the at least one neighboring vehicle. Once the at least oneneighboring vehicle has passed the vehicle, the at least one neighboringvehicle may become a leading vehicle, either in an airway adjacent tothe airway in which the vehicle is traveling or a leading vehicle thatis in front of the vehicle in the airway in which the vehicle istraveling, and the computer system may estimate the location of theairway of the road based on an estimated path of the leading vehicle, asdescribed above.

In some embodiments, the computer system may begin to monitor the atleast one neighboring vehicle only in response to determining that theairway information has become unavailable or unreliable. In theseembodiments, prior to determining that the airway information has becomeunavailable or unreliable, the computer system may rely solely on theairway information to estimate the location of the airway. In otherembodiments, however, the computer system may also monitor the at leastone neighboring vehicle prior to determining that the airway informationhas become unavailable or unreliable. In these embodiments, the computersystem may additionally use the distance to the at least one neighboringvehicle to estimate the location of the airway in which the vehicle istraveling. For example, if the at least one neighboring vehicle istraveling in an airway adjacent to the airway in which the vehicle istraveling, the computer system may determine that the airway does notextend to the at least one neighboring vehicle. As another example, ifthe at least one neighboring vehicle is traveling behind the vehicle inthe airway in which the vehicle is traveling, the computer system maydetermine that the airway includes the at least one neighboring vehicle.Other examples are possible as well. Alternatively, in theseembodiments, prior to determining that the airway information has becomeunavailable or unreliable, the computer system may simply use thedistance to the at least one neighboring vehicle to avoid collisionswith the at least one neighboring vehicle.

Further, in some embodiments, once the vehicle begins to monitor the atleast one neighboring vehicle, the computer system may stop using theairway information to estimate the location of the airway in which thevehicle is traveling. In these embodiments, the computer system may relysolely on the distance to the at least one neighboring vehicle to avoidcollisions with the at least one neighboring vehicle until the airwayinformation becomes available or reliable. For example, the computersystem may periodically attempt to obtain updated airway information.Once the computer system determines that the airway information hasbecome available or reliable, the airway information has becomeavailable or reliable, the computer system may once again rely on theupdated estimated location of the airway and less (or not at all) on thedistance to the at least one neighboring vehicle. The computer systemmay determine that the updated airway information is reliable when, forexample, the computer system determines that a confidence of the updatedairway information is greater than a predetermined threshold. Thepredetermined threshold may be the same as or different than thepredetermined threshold.

Another embodiment uses legs similar to horse legs and tromps throughhigh grass and uneven terrain while using sensor technology to closelyfollow the user. The system can follow a sensor attached to anelectronic device worn by a soldier, or can use object detection camerato follow the user. One embodiment uses electrical motor, and anotheruses a gas powered engine that drives a hydraulic pump, which in turndrives the hydraulic leg actuators. Each leg has four actuators (two forthe hip joint, and one each for the knee and ankle joints). Eachactuator unit consists of a hydraulic cylinder, servo valve, positionsensor, and force sensor.

In some embodiments, the system identifies obstacles on the road, andthe computer system may use one or more sensors to sense the obstacles.For example, the computer system may use an image-capture device tocapture images of the road and may detect the obstacles by analyzing theimages for predetermined colors, shapes, and/or brightness levelsindicative of an obstacle. The computer system may estimate the locationof the obstacle and control the vehicle to avoid the vehicle and yetmaintain a predetermined distance from neighboring vehicles in bothdirections. Other vehicles behind the lead vehicle can then simplyfollow the lead vehicle as part of a flock. The computer system may thencontrol the vehicle to maintain a distance between the vehicle and theat least one neighboring vehicle to be at least a predetermined minimumdistance to avoid colliding with the at least one neighboring vehicle.In another embodiment, the obstacle can be the result of a car accidentor emergency. The system automatically detects the occurrence of anemergency and provides safety at the scene. This is done by divertingtraffic flow near the point of emergency to a point where trafficresumes normal flow. The system secures the incident site to protectemergency personnel, their equipment and the public, from hazardousconditions at the scene and throughout the traffic control zone. Thesystem can establish a traffic control set-up that gives motoristsadequate warning and reaction time. The system also separatespedestrians from vehicular traffic and limits access to the site toauthorized persons only. One embodiment directs vehicles through anemergency traffic control zone with the following: Advance Warning Areashould alert vehicles that there is a traffic situation or difficultyahead which will require some action on its part; Approach area shouldidentify the nature of the equipment or vehicle that is about toencounter and allow them to analyze the situation; Transition Areashould provide an indication as to the expected action to be taken bythe vehicle to decide on a course of action and execute safe drivingtechniques prior to entering the Activity Area; and Activity Areaincludes Fend Off Position of the emergency vehicle, Buffer Zone (refersto scene protection area between the first emergency vehicle and theincident site), Incident Site (Restricted to authorized personnel only),Traffic Space (Area where traffic is allowed to pass by the ActivityArea), and Staging Area (Emergency Vehicles not immediately required toperform a function or shielding at the incident scene should be directedto stage in this area. The area should be downstream/upstream of theincident site and the location should not create a traffic hazard orobstruction). The system can determine a Termination Area from thedownstream side of the Staging Area to the point where normal traffic isable to resume. The information for an emergency is incorporated intothe 3D model for vehicular processing.

The system assists the flight control system 80 by identifying theobjects as potential “threats” and recommend options for the flightcontrol system 80. For example, the system can perform the following:

detecting an object external to a vehicle using one or more sensors;

determining a classification and a state of the detected object;

estimating the destination of the object;

predicting a likely behavior of the detected object based on priorbehavior data and destination;

responding with 3D evasive path options based at least in part on thelikely behavior of the detected object by moving up to next plane, downbelow current plane, or around the object; and

notifying the ground control or passenger of options based on the likelybehavior.

The process may cause the vehicle to take particular actions in responseto the predicted actions of the surrounding objects. For example, ifother flying vehicle is turning at the next intersection, the processmay slow the vehicle down as it approaches the intersection. In thisregard, the predicted behavior of other objects is based not only on thetype of object and its current trajectory, but also based on somelikelihood that the object may obey traffic rules or pre-determinedbehaviors. In another example, the process may include a library ofrules about what objects will do in various situations. The library maybe built manually, or by the vehicle's observation of other vehicles(autonomous or not) on the roadway. The library may begin as a humanbuilt set of rules which may be improved by the vehicle's observations.Similarly, the library may begin as rules learned from vehicleobservation and have humans examine the rules and improve them manually.This observation and learning may be accomplished by, for example, toolsand techniques of machine learning. In addition to processing dataprovided by the various sensors, the computer may rely on environmentaldata that was obtained at a previous point in time and is expected topersist regardless of the vehicle's presence in the environment. Forexample, the system can use highly detailed maps identifying the shapeand elevation of roadways, airway lines, intersections, crosswalks,speed limits, traffic signals, buildings, signs, real time trafficinformation, or other such objects and information. For example, the mapinformation may include explicit speed limit information associated withvarious roadway segments. The speed limit data may be entered manuallyor scanned from previously taken images of a speed limit sign using, forexample, optical-character recognition. The map information may includethree-dimensional terrain maps incorporating one or more of objectslisted above. For example, the vehicle may determine that another flyingvehicle is expected to turn based on real-time data (e.g., using itssensors to determine the current GPS position of another flying vehicle)and other data (e.g., comparing the GPS position with previously-storedairway-specific map data to determine whether the other flying vehicleis within a turn airway). These objects may have particular behaviorpatterns that depend on the nature of the object. For example, a bicycleis likely to react differently than a motorcycle in a number of ways.Specifically, a bicycle is more likely to make erratic movements whencompared with a motorcycle, but is much slower and thus can be handledwith ease compared to a speeding motorcycle. For each classification,the object data may also contain behavior information that indicates howan object having a particular classification is likely to behave in agiven situation. Vehicle may then autonomously respond to the objectbased, in part, on the predicted behavior.

Safety Monitoring

One function provided by the ATC 89 is safety. To provide safety withmany vehicles in the air, a safety system includes

a Ground control station to analyze flight data of a plurality of airvehicles, and

at least one air vehicle communicably coupled to the Ground ControlStation, the at least one vehicle being configured to:

capture statistical data for a flight route whilst flying substantiallyalong the flight route at least once, the statistical data pertaining toa plurality of flight parameters;

transmit, to the Ground Control Station, the statistical data for theflight route from the at least one vehicle;

capture flight data for the flight route and compare the flight datawith reference data from other vehicles flying substantially along theflight route or from a math simulation of expected flight parameters;

determine unexpected changes as prioritized by danger level and if anemergency exists, instructing the vehicle to go to a safe location.

Safety limits for various flight parameters during operation of thevehicle are captured. The system determines deviation of one or moreflight parameters below or above the safety limits in real time ornear-real time, thereby enabling execution of timely corrective actionto avoid failure and loss of the vehicle. Also, the method and system ofthe present disclosure updates the safety limits in near-real time basedon gathered flight data. Therefore, problems encountered duringoperation of the vehicle are detected promptly and addressed suitably.Furthermore, the use of various data (such as statistical data,reference data, flight data and environmental data) to update the flightparameters along the flight route, increases the reliability ofoperation of vehicle.

Optional Geo Restriction to Communication Towers

In one aspect, a flying vehicle includes: a cab with an optionalpassenger seat with optional steering control; a propulsion unit havinga rotating blade and an engine to rotate the blade; and a flight controlsystem that restricts the vehicle to communication range of a pluralityof communication towers or cells.

Implementations may include one or more of the following.

To conform to FAA regulations, the system can constrain flight to zonesand can include one or more of pre-configuring the plurality of vehicles10 to operate only where cellular coverage or WiFi exists, monitoringcell signal strength by the plurality of vehicles 10 and adjustingflight based on signal strength to stay within a range of thecommunication (com) towers.

The system can include the plurality of vehicles 10 and/or the pluralityof communication towers providing location, speed, direction, andaltitude. The location can be determined based on a combination oftriangulation by the plurality of cell towers and a determination by thevehicle 10 based on a location identification network. The plurality offunction can include one or more of separation assurance betweenvehicles 10; navigation assistance; weather and obstacle reporting;monitoring of speed, altitude, location, and direction; trafficmanagement; landing services; and real-time control. One or more of theplurality of vehicles 10 can be configured for autonomous operationthrough the air traffic control. The plurality of vehicles 10 can beconfigured with mobile device hardware configured to operate on aplurality of different cellular networks.

In an embodiment, the vehicle 10 maintains an association with at leastthree of the cell sites which perform triangulation to determine thelocation of the vehicle 10. In addition to the cell sites on the cellnetwork, the vehicle 10 can also communicate to the other wirelessnetworks 304. In an embodiment, the vehicle 10 can maintain its GPSand/or GLONASS location and report that over the cell network 302. Inanother embodiment, the other wireless networks 304 can includesatellite networks or the like.

The method can further include, during the emergency instructions,reestablishing communication to the ATC 89 via one of the primarywireless network and the backup wireless network; and receivinginstructions from the ATC 89 system. The primary wireless network caninclude a first wireless provider network and the backup wirelessnetwork can include a second wireless provider network. The firstwireless provider network and the second wireless provider network caninclude a cellular network, such as LTE. The vehicle 10 can include awireless interface configured to communicate to each of the firstwireless provider network and the second wireless provider network. Thecommunicating to the ATC 89 can include providing flight information tothe ATC 89 system; and receiving instructions and updates from the ATC89 for real-time control. The flight information can include weather andobstacle reporting, speed, altitude, location, and direction, and theinstructions and updates can relate to separation assurance, trafficmanagement, landing, and flight plan.

In another embodiment, the vehicle 10 is configured for networkswitchover to communicate with an Air Traffic Control (ATC) system. Thevehicle 10 includes one or more rotors disposed to a body and configuredfor flight; wireless interfaces including hardware and antennas adaptedto communicate with a primary wireless network and a backup wirelessnetwork of a plurality of wireless networks; a processor coupled to thewireless interfaces and the one or more rotors; and memory storinginstructions that, when executed, cause the processor to: communicate toATC 89 via the primary wireless network; receive and store emergencyinstructions from the ATC 89 system; detect communication disruption onthe primary wireless network to the ATC 89 system; responsive todetection of the communication disruption, switch to the backup wirelessnetwork to reestablish communication to the ATC 89 system; and,responsive to failure to reestablish communication to the ATC 89 via thebackup wireless network, implement the emergency instructions.

Mass Transit for Aerial Vehicles

In one aspect, aerial vehicles can land and hook to a mass transitsystem to be delivered at or near their destinations. This leverages thepublic transit infrastructure already present. In one embodiment, thetransit infrastructure is a rail above ground, but in other embodimentscan include maglev stations, for example. The vehicles would connect tocables on the rails.

Implementations may include one or more of the following. FIG. 2A showsan exemplary accelerator 50 for the vehicle 10 to move quickly at longdistances, for example from city to city, using little energy in anenvironmentally sustainable manner similar to a mass transit system. Thesystem includes a support structure 20, a horizontal track 30 connectedto the support structure 20, wherein the track 30 is positioned aboveground level, wherein the track 30 includes a plurality of airwaysthereon including bypass airways. A loading station 40 is accessiblefrom the first bypass airway and an unloading station accessible fromthe second bypass airway. The support structure 20 is used to supportthe track 30 above a ground level, which includes the ground surface,water surface, city obstructions, or various other structures extendingfrom the ground. The support structure 20 may also be used to supportthe track 30 through a tunnel, mountain, building, or various othertypes of structures that may be considered desired destinations or standin the way of the track 30. The support structure 20, because of themany types of terrain that the support structure 20 can encounter, maytake on a variety of shapes and configurations, as well as be comprisedof various types of materials. In one embodiment, the support structure20 includes a pair of vertical columns spaced apart to allow a vehicle10 to travel between. A horizontal structure or beam may be connected atupper end of the two columns 21, forming an inverted U-shapedconfiguration. The track 30 will be attached to the horizontal structureand the flying vehicle 10 suspended therefrom. It is appreciated that inthis configuration, multiple structures may be located along the track30, including a number of support structures 20 deemed necessary tosupport the track 30 and flying vehicles 10 traveling along the track30. The support structure 20 may also include a cable support systemsupporting the vertical beams overhead. The support structure 20 mayalso be configured to stretch over water surfaces, similar to a bridge.The support structure 20 may further be integrated with surroundingstructures, such as buildings, mountains, alternate cable supports, orvarious others, all which allow for the adequate support of the track30.

As shown in FIG. 2B, a plurality of vehicles 10 can be lifted onto thetrack 30 and then be flying by the accelerator 50 for travel along thetrack 30. Alternatively, the vehicle 10 can fly near the track 30 to berobotically hooked onto the track 30. It is appreciated that the flyingvehicles 10 may travel in-line with other vehicles 10, be connected toother vehicles 10, travel side-by-side with other vehicles 10 ormultiple other arrangements similar to highway systems and automobiles.The vehicles 10 are also preferably generally separated by apredetermined distance to prevent overloading of the support structures20 and to prevent vehicles 10 from engaging one another. The vehicles 10are further preferably automatically controlled to travel from locationto location thus reducing the amount of staff or hired operators neededto effectively utilize the mass transit system 50. In oneimplementation, vehicle 10 is rolled or driven to the loading station 40where it is loaded onto the horizontal track.

Turning back to FIG. 2B, an electrical contact 58 engages the elongatedelectrical cable 38 of the track 30 contact to provide electric power tothe plurality of wheels 51 which travel along the track 30 and otherelectrical components of the vehicle 10. The cable 38 runs parallel withthe track 30. Electrically powered vehicles 10 can then silently andcleanly travel within malls and office buildings for optimum convenienceto the travelling public. Having the vehicles 10 electrically poweredfrom a single supply source (or multiple supply sources) connected tothe track 30 also allows for the control and synchronization of themultiple vehicles 10. Of course the vehicles 10 may also include motorsor other power supplies. The wheels 51 generally extend from a wheel 51support 35 extending from the top side of the cab 11 of the flyingvehicle 10. Each of the wheels 51 are preferably angled inwards atsimilar orientations so that a groove 52 extending around the perimeterof the wheel 51 can receive the lower support cables 34 of the track 30at least partially within to provide stability to the vehicle 10traveling along the track 30. The wheels 51 thus are located above thesupport cables 34 of the track 30 and the cab 11 of the vehicle 10 islocated below the support cables 34 of the track 30. It is appreciatedthat other connection mechanisms or arrangements may be used to securethe vehicle 51 to the track 30. The vehicles 10 are able to travel atvarious different speeds in an energy efficient manner matching those ofmass transit systems. The speeds may be present at the installation ofthe vehicles 10 or may be adjusted via the passengers riding within thevehicles 10. The vehicles 10 are also preferably able to communicatewith each other so that a vehicle 10 knows if another vehicle 10 isstopping at a requesting stop, slowing down, traveling at a differentspeed, crossing tracks 30, or various other actions.

FIG. 2C shows an exemplary process for traveling with the vehicle 10 asfollows:

upload a flight plan to the flight control system of the vehicle (68)

fly to the track location (70)

connect the vehicle to high speed rail track (72)

lift the vehicle into the air in a vertical takeoff and landing mode(74)

move the vehicle to a rail track exit point (76)

releasing vehicle from the track near destination (78)

transitioning vehicle off the track (80)

move vehicle to final destination (82)

The first step involves uploading a flight plan to the flight controlsystem of the vehicle 10 in 68. The vehicle may now be operatedresponsive to autonomous flight control, remote flight control or acombination thereof. Regardless of flight control mode, the next step isdispatching the selected vehicle from the transportation servicesprovider location to the current location of the pod assembly to betransported, as indicated in block 70. This step may involve flying fromthe current location to the location of a track 30 provided by atransportation services provider, identifying a landing zone proximatethe current location of the track 30, performing an approach andlanding, then positioning the vehicle 10 relative to the track 30 toenable attachment therebetween. The next step is coupling the vehicle totrack 30, as indicated in block 72. The process of coupling the vehicleto the track 30 may be autonomous, manual or a combination thereof. Inany case, the coupling process including forming a mechanical connectionand preferably establishing a communication channel therebetween. Thevehicle may now be operated responsive to autonomous flight control,remote flight control, onboard pilot flight control or a combinationthereof. Once the track 30 is properly coupled to the vehicle 10, thetrack lifts the vehicle into the air in a vertical takeoff and landingmode, as indicated in block 74. During the track travel, the vehicle 10is preferably maintained in a generally horizontal attitude. Once thevehicle has reached track 30, the next step is moving the vehicle to amass exit point, as indicate in block 76. In the example of FIG. 2A, themass transit is from San Francisco to San Jose terminal. The processtransports the vehicle to the desired intermediate destination locationand then releasing the vehicle from the track, as indicated in block 78.As the vehicle approaches the destination, the next step istransitioning the vehicle off the track, as indicated in block 80.Preferably, this transition involves keeping the vehicle remains in thegenerally horizontal attitude. The next step is to traverse the vehicleto final destination (through driving or flying and landing the vehicleat the destination), as indicated in block 82. This step may involveidentifying a landing zone and performing an approach in the verticaltakeoff and landing mode.

Integration with Carriers

In one aspect, a flying vehicle includes: a cab to contain items to bedelivered; a propulsion unit having a rotating blade and an engine torotate the blade; a rail from a cab top extending toward one externalside of the cab, the cab having a moveable actuator coupled to thepropulsion unit to move the propulsion unit between a first positionabove the cab during lift-off and a second position during lateral(forward or backward) flight. The vehicle integrates with a freighttransportation marketplace to easily transition legacy freighttransportation with the drones.

Implementations may include one or more of the following.

A method and apparatus supporting an automated closed loop freighttransportation marketplace are described. The system provides theability for a broker to see on a map available carrier for transportingloads and conversely for carriers to see brokers or freight owners whoneed transportation services.

FIG. 3A shows an on-line autonomous freight marketplace with a pluralityof private networks 1-n. Shippers and their agents prefer to offer loadsdirectly to transport carriers they trusted with good safety records.Shippers also wanted an easy to use system that would eliminate multiplephone calls and paper work . . . all at a good price. The system allowseach shipper and/or an agent a private network of pre-qualified,preferred safe transport carriers with good safety scores. To facilitatethe transaction, the system allows freight quoting and bidding,electronic signature features, electronic freight document facilitation,exchange, transmission and data storage. Vehicle 10 with load offersreputable shippers with an affordable and easy to use system thatsimplified vehicle's busy schedules and give the carriers thecredibility of being prequalified, preferred with a good safety rating.The transportation marketplace automatically matches a shipper's freightwith empty transport vehicles in the private networks 1-n, givingshippers private fleet dependability at backhaul pricing. The systemallows users to manage consistent and repetitive airways to ensurefreight gets where it needs to be on time and vehicles return as needed.Shippers can move freight and business toward sustainability, costsavings, and transportation reliability with the private networksPN1-PNn.

FIG. 3B shows an exemplary environment for matching vehicle 10 s toloads. A load owner 1 (such as a manufacturer who needs to shipproducts) searches a load book database computer 3 for available loadvehicles. The database computer 3 searches a second database computer 5for available capacity by location and/or need. The database computer 3looks for available vehicle from database computer 5, and in responsethe computer 5 returns matching vehicle(s). The database computer 5communicates vehicle status over the Internet 6 and such vehicle statusand book load information can be wireless communicated using cellulartowers 7, for example to a plurality of subscribers 8A-8D in variouslocations. In this example, the system matches flight control system 809 to load 8C based on proximity to load and vehicle requirements.

Next an exemplary process to match shippers to vehicle 10 is disclosed.The process includes determining one or more vehicles 10 proximal to ageographical location of a shipping load, each shipping load having ashipping profile. The process then retrieves a profile of each nearbyvehicle and compares the vehicle profile with the shipping profile toidentify one or more matching vehicles. The process then contactsmatching vehicle flight control system 80 about the shipping load. In acorresponding user interface for a vehicle 10 looking for a customer, anumber of shipping prospects are offered to the vehicle computer, wherethe vehicle can retrieve each prospect's desired load capacity, type ofload, the start and destination addresses, and desired delivery date andcontact the prospect and/or provide a quote if there is a match. Theprocess can include ratings by other vehicle owners and shippers oftheir respective delivery or payment performance. Posting capacity canbe done without the time-consuming search of vehicle boards. Theinformation is centralized so that once done, the vehicle 10availability, profile, and capacity information can be viewed by avariety of users and available much sooner to the freight ownercomputer. The system helps vehicle 10 s find perfect client ahead oftime. Vehicle 10 can build loyalty with large shipper or freightbrokerage who pays well and who needs services regularly. The ratingsystem allows vehicle 10 s and shippers to operate on the basis ofquality and service rather than being completely focused on getting thecheapest rate all the time.

FIG. 3C is a network diagram depicting an online shipping servicetransaction processing system for an automated transportationmarketplace. A load book 10 captures available loads for pick up, bookedloads, delivered loads, deliveries waiting for payment, and paid/closedcases. The load book 10 is reviewed by a broker, which in turn can havesub-accounts each with a load portfolio manager, for example. The loadbook 10 also communicates with a plurality of carriers 30 throughdesktop computers, mobile computers, smart phones, among others. Thecarriers 30 can interact with the broker 20 to form contract directly,or can communicate through the load book 10 to offer and make/accept theoffers. The carriers can also communicate load confirmation, pick upconfirmation, provide tracking dta, check calls, provide deliveryconfirmation, signs bill of lading, and receive payments to close out acontract, among others. The load book 10 and the broker 20 can load datainto a customer transportation management system (TMS) 50 to post loads,receive load booking, receive pick-up and delivery information, andpay/close a shipping case. The load book 10 communicates with amarketplace data mart 40 which provides profiles of vehicle 10 s, amongothers. The marketplace 40 receives location updates from each vehicleas the vehicle moves. Alternatively, the location can be communicatedwhen a flight control system 80 posts his/her location using a postvehicle button 72 on a smartphone 70 running an application thereon. Theapplication also captures vehicle capacity and owner profiles, and suchinformation is wirelessly uploaded to the marketplace 40. The profileinformation is typically entered once, and the capacity information canbe entered once, and available capacity can be periodically updateddepending on the utilization of the vehicle during a particular trip,for example. Using wireless communication protocols, location update andcapacity information can be updated in real-time. In one embodiment, thevehicle 10 inputs the information on his/her capacity into a templatesupplied by the load board and may include type of equipment, amount ofdeadhead mileage the owner is willing to travel to position the capacityto the origin of the load, current location of the capacity and wherethe owner of the capacity would like for the capacity to end up. The webclient can access various marketplace and payment applications via theweb interface supported by the load book 10. Similarly, a programmaticclient accesses the various services and functions provided by themarketplace and payment. The programmatic client may, for example, be anapplication to enable vehicle 10 s to author and manage vehicle servicelistings on the marketplace 40 in an off-line manner, and to performbatch-mode communications between the programmatic client 30 and thenetwork-based marketplace 40.

Turning now to FIG. 3D, a broker initiates a shipment (102) by enteringload details (104). The load is set up in a Broker TMS DB entry 106. TheTMS sends load details to load book (108). The load details are enteredinto a load book (110). The system checks if the load is to beadvertised to all approved carriers (112). If so, the system adds theload to the available load list (114) and approved carriers viewavailable loads to bid on their computer or mobile phone (116). From112, if specific carriers from a selected list of Private Networks areto be used, the system sends bidding data to carriers based on airway,equipment, and location, among others (118). The carriers or vehicle 10s in the selected list of 118 then can review and prepare bids inresponse to the request (120). From 116 or 120, the vehicle 10 checks ifthe load details are acceptable (122) and if so accepts and proceeds to132. Otherwise the vehicle 10/carrier proposes a new price (124) andchange the load status to “New Offer” (126). The broker display picks upthe new carrier/vehicle 10 offer (128) and makes a decision. If theoffer is rejected in 130, the price negotiation is continued in 124 andotherwise the system proceeds to 132 and updates the load status as“Accepted”. The carrier acceptance is also displayed on the brokerdevice (134) and the broker tenders the load to the carrier or vehicle10 (136) and the status is correspondingly updated as “Tendered” (138)The system then updates load status in TMS (140) and the TMS load statusis updated (142). The carrier then accepts the load (144) and the brokerinitiates a load confirmation (146) and the broker TMS generates theload confirmation (148), or alternatively the system generates a loadconfirmation indication (150). In either case the carrier receives adigital load confirmation (152). The carrier electronically signs theload confirmation (154) and the load status is updated in the TMS (156),as completed in 158. From 154, if the carrier confirms pick up in amobile app (160), the system updates the load status in the TMS (162),and the TMS load status is updated (164). From 146, the systemdetermines if tracking is required (170), and if so, the system notifiesthe carrier that tracking is needed (172). In response the carrier turnson location broadcasting on the vehicle carrying the load (174). From170 or 174, the broker decides if tracking is to be enabled (180) and ifso, sets a tracking interval (182). The load book is updated as thesystem pings the carrier device and updates the current location (184)based on the device location sent at the predetermined interval (188),and the most recent carrier location is shown on the broker screen(186). From 180, the system also checks if the broker desires thevehicle call feature to be enabled (190). If so, the load book isupdated to reflect that calls or text messaging (SMS) result is logged(192). Correspondingly, the carrier/vehicle responds to calls ormessages from the shipper or broker (194). When the carrier arrives thatthe delivery location (196), the system checks if tracking is enabled(198) and if so, the system recognizes the carrier arrival at thedelivery location (200) and updates the arrival of the shipment on thebroker screen (202). Otherwise, the carrier confirms load delivery inthe phone app (204), and updates the load status to “Delivered” (206),and the load delivery status is displayed on the broker screen (208).The system also sends a signal to update the TMS (210), and the TMSstatus is updated (212).

Turning to FIG. 3E, from 204, the system updates the TMS with a “LoadDelivered” status (220). The load delivery information is displayed onthe broker screen (222) and the TMS load status is also updated (224).The load book sub-system also prompts the carrier for a freight bill oflading (BOL) (226). The BOL works as a receipt of freight services, acontract between a freight carrier and shipper and a document of title.The bill of lading is a legally binding document providing the flightcontrol system 80 and the carrier all the details needed to process thefreight shipment and invoice it correctly. In one embodiment, thefreight bill of lading is automatically generated based on the shipmentdetails entered during the quoting and booking process. The bill oflading should be provided to the carrier on pick up and will bedelivered to the consignee on delivery. The BOL can include thefollowing:

Shipper's and receiver's/consignee's names and complete addresses.

PO or special account numbers used between businesses for ordertracking.

Special instructions for the carrier to ensure prompt delivery.

The date of the shipment.

The number of shipping units.

Type of packaging, including cartons, pallets, skids and drums.

A note if commodity is a Department of Transportation hazardousmaterial. Special rules and requirements apply when you are shippinghazardous materials.

A description of the items being shipped, include the material ofmanufacture and common name.

The freight classification for the items being shipped.

The exact weight of the shipment. The weight of each commodity is listedseparately if multiple commodities are being shipped.

The declared value of the goods being shipped.

The carrier can photograph a signature confirming the delivery as proofin the app (228), and sends the photo of the signed BOL from the app(230). The system formats that BOL and photo updates the load status(232). The broker screen shows the “Signed BOL” status (234). Next, thesystem checks if the receipt is acceptable (236). If so, the broker paysthe carrier in the TMS (238) and approves payment or releases the fundsto the system to pay the carrier (242). Otherwise the broker manuallyresolves the issue with the carrier (240). The load book correspondinglyapproves payment or releases the funds to the system to pay the carrier(244) and the system pays the carrier based on contract requirement(246). The load-book system updates the load status (248), and sets caseas closed and sends the file to a data mart (252) and stores in thehistorical data mart (254) and exits. The TMS load status is alsoupdated (250), and from 238 or 246, the funds are posted to the carrieraccount (260).

FIGS. 3F and 3G show exemplary processes for adding a carrier to aprivate network and the public network, respectively. In FIG. 3H, abroker selects one or more carriers as “Approved” for one or moreprivate networks (302). The TMS updates its carrier data file (304), andthe private network files are updated (306). The carrier receives aninvitation to join the network (308). If the carrier accepts in 310, theprivate network carrier status is updated as “Active” (312). The carrieractive indication is displayed on the broker screen (314). The processthen checks if the broker wishes to see all loads (316), and if not, the“Carrier Active” is displayed on the broker screen (318) and otherwisethe private networks are requested with a status update for all loads(320).

FIG. 3I shows an exemplary process to add carriers to an approvednetwork. In one embodiment, the TMS contains the carrier data file (402)which is consulted when the broker marks a new carrier as “Approved” forthe vehicle loads (404). The status is updated as “Approved” (406) andVehicleload data is updated (408). Eventually, the broker needs to shipa load and searches for posted vehicles (410), and the “Approved”vehicle is displayed in a differentiated way on a map display (412). Forexample, “Approved” vehicles can be displayed as green color vehiclesand the remaining vehicles are in black color, as illustrated by FIG.1C. The broker can select one of the Approved vehicles (414) and if sothe process allows the broker to tender the load to the carrier (416).On the carrier side, the carrier can update the system when the tenderis received (418).

In one embodiment, the system collects information in advance from thecapacity owner, who would note the type of equipment, the desired amountof deadhead miles needed to reposition the equipment, the desireddestination and the amount the capacity owner needed to be profitablewhen moving the freight. With this information already captured andstored in a carefully designed web based platform, the capacity owner'sexact location is gleaned from his/her smartphone (or electronichandheld device) and then would be transmitted to the freight ownerthrough the available cell network to the freight owners web portal with‘one click’ of a digital button on the capacity owner's handheld device.Thus the capacity owner would post his/her available capacity to atransportation marketplace with a large number of freight owner membersin a matter of milliseconds making the capacity available for use by aready and willing freight owner population. In addition, should thecapacity owner reposition his/her vehicle, the GPS would track thesmartphone location and transmit it back to the transportationmarketplace, keeping the freight owners up to date on the location ofthe empty capacity.

Vehicle 10 s and customers/brokers who have entered into a transactioncan rate each other at the end of the transaction. A Feedback score isthen attached to each member profile. The Feedback score is one of themost important pieces of a Feedback Profile. The Feedback score is thenumber in parentheses next to a member's username, and is also locatedat the top of the Feedback Profile. Next to the Feedback score, the useror member may also see an icon such as a vehicle with colors. The numberof positive, negative, and neutral Feedback ratings a member hasreceived over time are part of the Feedback score. For each transaction,vehicle 10 s and shippers/brokers can choose to rate each other byleaving Feedback. Shippers/Brokers can leave a positive, negative, or aneutral rating, plus a short comment. Vehicle 10 s can leave a positiverating and a short comment.

The system can support one or more features or functions on a websitehosted by the third party. The third party website may, for example,provide one or more promotional, marketplace or payment functions thatare supported by the relevant applications of the freight marketplace40.

The marketplace 40 itself, or one or more parties that transact via themarketplace 40, may operate loyalty programs that are supported by oneor more loyalty/promotions applications. For example, a shipper orbroker may earn loyalty or promotions points for each transactionestablished and/or concluded with a particular vehicle 10 or carrier,and be offered a reward for which accumulated loyalty points can beredeemed.

The Vehicle Web site host may be configured to enable the user to viewreputation information (e.g., feedback) with respect to another user.The user may request to view the reputation information associated withthe opposite transacting party. Alternatively, the user may be presentedwith the relevant reputation information associated with the oppositetransacting party responsive to the user's request to enter into atransaction with another user. The overall view may be provided to theuser according to the requesting user's preferences stored in “UserPreferences”. A view of associated reputation information is thenretrieved from the feedback score table stored in “Feedback Score” andthe feedback left score table stored in “Feedback Left Score”. If theuser desires details, then the details may be presented (e.g., inpaginated format) utilizing “Feedback Detail History Overall” and“Feedback Detail History”. The user may be enabled selectively to accessreputation information according to criteria such as promptness ofpayment information, quality of performance information, timeliness ofperformance information, or promptness of response information as wellas according to other criteria. Other criteria may be, but not limitedto, shipping, packaging, item accurately described, promptness ofleaving feedback, was the item returned by the shipper, was there anon-payment, when was the item received, etc.

In another aspect, a method includes communicating to one or morevehicles over one or more wireless networks; receiving a deliveryrequest from a company specifying a pickup location, a package, and adelivery location; directing a vehicle 10 to pick up the package at thepickup location and to deliver the package to the delivery location,wherein the company computer generates a flight plan to the vehiclebased on the delivery request.

The drone method can further include receiving a second delivery requestfrom a second company specifying a second pickup location, a secondpackage, and a second delivery location; selecting a second vehicle 10of the one or more vehicles 10 for the delivery requests; and directingthe second vehicle 10 to pick up the second package at the second pickuplocation and to deliver the second package to the second deliverylocation, wherein the air traffic control system provides a secondflight plan to the second vehicle 10 based on the second deliveryrequest. The process assigns the vehicle 10 a specified flying airwayand ensuring the vehicle 10 is within the specified flying airway basedon the communicating. The method can further include receivingphotographs and/or video of the delivery location subsequent to deliveryof the package; and providing the photographs and/or video as a responseto the delivery request. The directing can include providing a deliverytechnique comprising one of landing, dropping via a tether, dropping toa doorstep, dropping to a mailbox, dropping to a porch, and dropping toa garage.

In another aspect, a computer-implemented method to match a flyingvehicle to a company with a load to ship, comprising:

identifying one or more carriers as a preferred vendor or a privatenetwork in a database;

tracking a geographical location of a vehicle from the private networkof vendors;

determining one or more shipping loads proximal to the geographicallocation of the vehicle, each shipping load having a shipping profile;

retrieving a vehicle profile;

comparing the vehicle profile with each shipping profile to identify oneor more matching loads; and

notifying a vehicle flight control system of matching loads.

Implementations may include one or more steps of: sending a notificationto the device of a nearby position of the load, the nearby position ofthe load related to one or more categories of interest, the notificationsent in response to proximity of the geographical location of the devicerelative to the position of the load.

specifying the category of interest manually or learning the category ofinterest automatically.

applying geo-fencing to trigger proximity of the vehicle flight controlsystem device to the position of the load.

automatically discovering new loads or points of interest in proximityto the vehicle flight control system device based on a detected changein the geographical location of the vehicle flight control systemdevice.

creating and updating a repository that includes a category of interestin association with the vehicle flight control system device,geolocation information for movable loads, temporary loads, and newloads.

creating and presenting a list of points of interest on the vehicleflight control system device as the notification to the vehicle flightcontrol system 80 and for vehicle flight control system interaction.

collecting feedback on delivery performance.

requesting generic rating information related to a load delivery.

requesting specific rating information related the plurality ofperformance categories.

In yet another aspect, method to expedite a shipping transaction onbehalf of a shipper, comprising:

using a freight broker computer or mobile device, identifying a list ofvehicles driving between a source and a destination;

determining one or more favorite vehicle 10 s from a favorite vehicle 10list;

sharing transaction information only with favorite vehicle 10 s forpricing negotiation;

negotiating an expedited agreement based on the shared transactioninformation; and

selecting one of the favorite vehicle 10 s for the shipping transaction.

Implementations may include one or more of:

displaying the favorite vehicle 10 s and the source.

displaying the favorite vehicle 10 s and the destination.

automatically notifying vehicle 10 s near the source to bid on theshipping transaction.

automatically notifying favorite vehicle 10 s near the source to bid onthe shipping transaction.

providing financial information relating to the transaction.

communicating transaction information with mobile devices associatedwith the favorite vehicle.

rating the favorite vehicle by the shipper or a broker.

periodically replenishing the favorite vehicle 10 list by selecting ahighly rated vehicle 10 not in the favorite vehicle list.

determining a selected vehicle below a predetermined rating thresholdand removing the selected vehicle from the favorite vehicle list.

Aerial Product Pickup/Delivery

In one aspect, a flying vehicle includes: a cab to contain products tobe delivered; a propulsion unit having a rotating blade and an engine torotate the blade; a rail from a cab top extending toward one externalside of the cab, the cab having a moveable actuator coupled to thepropulsion unit to move the propulsion unit between a first positionabove the cab during lift-off and a second position during lateral(forward or backward) flight.

Implementations may include one or more of the following.

The drone delivery service can manage delivery for a variety ofproviders enabling drone delivery for smaller providers. Further, theATC system can be used to schedule, manage, and coordinate pickup,distribution, delivery, and returns.

FIG. 4A-4B shows exemplary views of a drone or UAV with releasableproduct clamps. This embodiment is similar to those in FIGS. 1A-1B,except the embodiment is focused on carrying a container. For parceltransport, a plurality (such as 4) cabins can be linked together forincreased capacity. The vehicle 10 has a propulsion unit 12 having arotating blade and an engine to rotate the blade. The vehicle includes arail 16 from a container top extending toward one external side of thecontainer. The rail or the cab has a moveable actuator coupled to thepropulsion unit 12 to move the propulsion unit 12 between a firstposition above the cab during lift-off and a second position behind orin front of the container during forward flight or backward flight. Thecontainer can be secured to the propulsion unit 12 with robotic grabber8 that can automatically secure or release the container during dropoff. Alternatively, the propulsion unit 12 can be manually strapped tothe container for low cost.

FIG. 4C shows the control electronics. Since the parcel delivery has acost constraint, the drone offloads processing to edge computers on thecellular network. 5G networks have low latency so the processing can bedone and returned in time to navigate the drone. The control is low costand includes one or more cameras 85, accelerometer/gyroscope 86, a motorflight control system 80 with motor 87, a cellular/wifi transceiver 87,and a processor serving as flight control system 80. The processoroptionally controls a heater 83 that provides heat to an optionalballoon 85 to cause the drone to float mid air and reduces powerrequired by the motor 87. The drone communicates and is controlled by aplurality of ATC 89 located on cell towers or Wifi communication towers,among others.

The ATC 89 can maintain data such as location information received andupdated periodically from each of the plurality of vehicles 10, andwherein the location information is correlated to coordinates andaltitude. The location information can be determined based on acombination of triangulation by the plurality of cell towers and adetermination by the vehicle 10 based on a location identificationnetwork. The processing for the delivery application authorization andmanagement can include checking the coordinates and the altitude basedon a flight plan, for each of the plurality of vehicles 10. The checkingthe coordinates and the altitude can further include assuring each ofthe plurality of vehicles 10 is in a specified flying airway.

In one embodiment, the ATC 89 utilizing wireless networks andconcurrently supporting delivery application authorization andmanagement includes the processor and the network interfacecommunicatively coupled to one another; and the memory storinginstructions that, when executed, cause the processor to: communicate,via the network interface, with a plurality of vehicles 10 via aplurality of cell towers associated with the wireless networks, whereinthe plurality of vehicles 10 each include hardware and antennas adaptedto communicate to the plurality of cell towers; maintain data associatedwith flight of each of the plurality of vehicles 10 based on thecommunicating; process the maintained data to perform a plurality offunctions associated with air traffic control of the plurality ofvehicles 10; and process the maintained data to perform a plurality offunctions for the delivery application authorization and management foreach of the plurality of vehicles 10.

For delivery drones, the airway management aims to manage deliveriesefficiently while secondarily to ensure collision avoidance. The ATC canimplement various routing techniques to allow the vehicles 10 to useassociated airway to arrive and deliver packages. Thus, one aspect ofairway management, especially for delivery applications, is efficiencysince efficient routing can save time, fuel, etc. which is key fordeliveries.

The delivery airway covers all flight phases which include preflight,takeoff, en route, descent, and landing. Again, the airway includescoordinates (e.g., GPS, etc.), altitude, speed, and heading at aspecified time. As described herein, the vehicle 10 is configured tocommunicate to the ATC, during all of the flight phases, such as via thetransceivers and communication networks. The ATC is configured tomonitor and manage/control the airway as described herein. The objectiveof this management is to avoid collisions, avoid obstructions, avoidflight in restricted areas or areas with no network coverage, etc.

During preflight, the vehicle 10 is configured to communicate with theATC for approvals (e.g., flight plan, destination, the flying airway,etc.) and notification thereof, for verification (e.g., weather,delivery authorization, etc.), and the like. The key aspect of thecommunication during the preflight is for the ATC to become aware of theflying airway, to ensure it is open, and to approve the vehicle 10 fortakeoff. Other aspects of the preflight can include the ATC coordinatingthe delivery, coordinating with other systems, etc. Based on thecommunication from the vehicle 10 (as well as an operator, scheduler,etc.), the ATC can perform processing to make sure the airway isavailable and if not, to adjust accordingly.

During takeoff, the vehicle 10 is configured to communicate with the ATCfor providing feedback from the vehicle 10 to the ATC. Here, the ATC canstore and process the feedback to keep up to date with the currentsituation in airspace under control, for planning other airway, etc. Thefeedback can include speed, altitude, heading, etc. as well as otherpertinent data such as location (e.g., GPS, etc.), temperature,humidity, wind, and any detected obstructions during takeoff. Thedetected obstructions can be managed by the ATC as described herein,i.e., temporary obstructions, permanent obstructions, etc.

Once airborne, the vehicle 10 is en route to the destination and the ATCis configured to communicate with the ATC for providing feedback fromthe vehicle 10 to the ATC. Similar to takeoff, the communication caninclude the same feedback. Also, the communication can include an updateto the airway based on current conditions, changes, etc. The vehicle 10is continually in data communication with the ATC via the networks.

As the destination is approached, the ATC can authorize/instruct thevehicle 10 to begin the descent. Alternatively, the ATC canpre-authorize based on reaching a set point. Similar to takeoff anden-route, the communication in the descent can include the samefeedback. The feedback can also include information about the landingspot as well as processing by the ATC to change any aspects of thelanding based on the feedback. The landing can include a physicallanding or hovering and releasing cargo.

The landing authorization and management can be at the home base of thevehicle 10, at a delivery location, and/or at a pickup location. The ATCcan control and approve the landing. For example, the ATC can receivephotographs and/or video from the vehicle 10 of the location (home base,delivery location, pickup location). The ATC can make a determinationbased on the photographs and/or video, as well as other parameters suchas wind speed, temperature, etc. to approve the landing.

For example, a vehicle 10 from the delivery operator can fly to variousdistribution/pickup locations as instructed by the ATC over wirelessnetworks for package pickup and delivery to various delivery locations,e.g., homes, offices, etc. The delivery operator can provide a deliveryservice which supports multiple different distribution/pickup locationssuch as for different companies. That is, the delivery service cansupport various companies in a geographic region, supported by the ATC89. The delivery service can be for smaller companies who cannot buildtheir own drone fleet. For example, the delivery service can be similarto parcel delivery services, albeit via the vehicles 10. Of course,other embodiments are contemplated. Also, it is contemplated that thevehicle 10 can handle multiple packages at the same time, including fromdifferent distribution/pickup locations and with different deliverylocations. The vehicle 10 and camera 85 can aim to drop the package infront of a door. Further, if the camera 85 detects that the blade maycause personal injury, the vehicle 10 performs emergency shutdown of themotor 87, even if it means damage to the vehicle and to the content itcarries to avoid personal injury.

As shown in FIG. 4D, a warehouse system 51 routes the container of thevehicle 10 through various assembly stations to put the content to bedelivered into the container. The warehouse 51 has an area may bedesignated for the loading and unloading of pods carrying parcels to betransported by drones to other exchange stations in order to reach afinal destination. The warehouse system is connected to a passengerterminal such as terminal 50 (FIG. 2A) so that items from long rangeports or terminals such as Oakland/LA ports using rail/truck transportto the warehouse 51. It will be appreciated that “terminals” or “publictransportation infrastructure terminals” as used herein are intended toinclude, but are not limited to, bus stops, train stations, harbors,shipyards, monorail stations, airports, vehicle charging stations, andsubway stations. Furthermore, it will be appreciated that “vehicles” or“public transportation infrastructure vehicles” as used herein areintended to include, but are not limited to, trains, buses, cars,monorails, subways, flight control system 80, bullet trains, boats,subways, and planes. Moreover, it will be appreciated that “drones” asused herein are intended to include Unmanned Aerial Vehicles (“UAVs”),quadcopters, quadrotors, unmanned aircraft, and multirotor unmannedaerial vehicles. The system includes a support structure 20, ahorizontal track 30 connected to the support structure 20, wherein thetrack 30 is positioned above ground level, wherein the track 30 includesa plurality of airways thereon including bypass airways. A loadingstation 40 is accessible from the first bypass airway and an unloadingstation accessible from the second bypass airway. The support structure20 is used to support the track 30 above a ground level, which includesthe ground surface, water surface, city obstructions, or various otherstructures extending from the ground. The support structure 20, becauseof the many types of terrain that the support structure 20 canencounter, may take on a variety of shapes and configurations, as wellas be comprised of various types of materials. In one embodiment, thesupport structure 20 includes a pair of vertical columns spaced apart toallow a vehicle 10 to travel between. A horizontal structure or beam maybe connected at the upper end of the two columns 21, forming an invertedU-shaped configuration. The track 30 will then be attached to thehorizontal structure and the carrier vehicle 10 suspended therefrom. Itis appreciated that in this configuration, multiple structures may belocated along the track 30, including a number of support structures 20deemed necessary to support the track 30 and carried vehicles 10traveling along the track 30. The support structure 20 may also includea cable support system supporting the vertical beams overhead.

In some embodiments, the delivery system employs a “top-down”architecture that centrally determines the drone delivery routes,vehicles, terminals, and drones utilized for public infrastructurefacilitated drone delivery. For example, in such a “top-down” baseddelivery system, the delivery system determines the packages and dronesthat are placed on specific vehicles, and which packages depart fromcertain terminals at certain times at the beginning of each day beforedelivery commences.

In other embodiments, the delivery system employs an “emergent” or“bottom-up” architecture that uses rules to govern behavior of thedelivery system. For example, in an “emergent” based delivery system,local servers determine drone delivery routes (e.g., once a package anda drone arrive at a terminal, a server on the terminal parses throughlocal geolocation data to determine a drone delivery route). Moreover,in an example “emergent” based delivery system, individual components ofthe delivery system recalculate the next delivery “step” after eachprevious “step” is completed (e.g., once a drone delivers a package andreturns to a terminal, the delivery system determines a next vehiclearriving at the terminal that needs more drones to deliver packages, andthe system will then send an instruction to the drone to board theidentified vehicle when the identified vehicle arrives at the terminal).In example “emergent” embodiments, the delivery system identifies avehicle to pick up a drone from a terminal after delivery based on oneor more of: a minimum time for a vehicle to arrive at the terminal,number of packages on board each vehicle, and number of drones on boardeach vehicle.

Drone offload exchange stations comprises a set of loading bays, eachloading bay may have one or more separate loading docks for drones todrop off pods. Backup docks may be implemented in exchange stations thatexpect a higher volume of drone traffic enable multiple drones todrop-off pods simultaneously. It should be understood that althougheight loading bays are illustrated in this embodiment, it may bepossible to have as few or as many loading bays as needed as spaceallows. Loading bays may be arranged in such a fashion that they may belocated towards the direction of respective exchange stationdesignation. This may reduce the number of flight paths crossing asdrones take off and fly to their designated exchange stations. Dronesmay land at a target loading bay, and after offloading all pods, maypark in a designated drone parking area, a 4-pod drone bay, or a 1-poddrone bay. This embodiment only utilizes one of each drone bay, but itmay be possible to have as many as needed to provide space for dronesthat may be on standby. In a future embodiment in which drones ofvarious shapes and sizes are introduced, there may be more parking areasdesignated for each drone type, or one area may be designated for mixeddrone parking.

While it is generally simple to consolidate numerous packages and movelarge numbers of parcels between large freight stations, it is oftenmuch less efficient to transport smaller numbers of goods to their finaldestinations. In various example embodiments described herein, drones(e.g., Unmanned Aerial Vehicles or “UAVs”) are utilized to increase theefficiency of “last mile” delivery. Given the limitations of batterytechnologies, drones have a limited range and often need to be rechargedonce their batteries, or another energy source, are depleted. This hasthe potential to limit the areas in which drone delivery is possible tolocations within a short distance of warehouses or freight stations.Furthermore, various example embodiments described herein can lessendrone traffic, which may be an eyesore as well as a safety risk. Exampleembodiments contemplate the use of existing public transportationinfrastructure to deliver packages and drones to a particular publicinfrastructure terminal (e.g., a bus stop) where drone deliverycommences, which can highly extend the effective range of drone deliverysystems and alleviate some drone traffic.

Moreover, example embodiments described herein can lower setup costs ofdrone delivery by utilizing existing infrastructure. For example, a bustraveling along an existing bus route can deliver drones and packages toa bus stop within a predetermined radius of a package destination, andone or more drones can deliver one or more packages from the bus stop tothe package destination.

Various example embodiments, using drones to deliver packages, allow forpackage delivery in areas difficult to reach. For example, variousembodiments allow for package delivery to a roof, windowsill of amulti-story building or apartment, a backyard, a user-specifiedgeolocation, or directly to a current user geolocation based on ageolocation of a user device (e.g., as determined via a GPS component ofa mobile device such as a cellphone).

To initiate the process of drone delivery, in some embodiments, thedelivery system receives a request, submitted by a user, to deliver anitem using a drone (e.g., when the user purchases or posts an offer tosell the item on an e-commerce website). In a specific example, once theuser submits his or her payment information on an e-commerce website,the user is directed to a shipping options page that prompts the user toinput a delivery address and provides the user with an option for dronedelivery of the item. In such example embodiments, the delivery systemreceives this request for drone delivery of the item including locationinformation of a drop-off destination. In some embodiments, the drop-offdestination is a set physical location, such as a home address or anoffice address. In some embodiments, the drop-off destination is linkedto geolocation data detected at a user device of the user (e.g., asmartphone, a wearable device, or a user's personal vehicle). Forexample, the user downloads an application on her smartwatch andprovides permission via the application for the delivery system toaccess the geolocation data detected at the smartwatch of the user, andthe delivery system uses the geolocation data of the smartwatch as thedrop-off destination.

In some embodiments, the delivery system communicates an instruction tocause the package to be transported by the vehicles to a final terminal(e.g., a bus stop, train depot, or subway station). In exampleembodiments, the drone delivery system determines a drone delivery routeand communicates an instruction to the drone to deliver the package tothe drop-off destination via the determined drone delivery route.

In various embodiments, the delivery system accesses publicinfrastructure information including public infrastructure terminallocations and public infrastructure vehicle route information (e.g.,“vehicle route information”). In some embodiments, the vehicle routeinformation includes timing information or scheduling information.

Consistent with some embodiments, once the system determines thedrop-off destination and a specified delivery time or range of times,the delivery system identifies a public infrastructure terminal fromwhich the drone delivers the package to the drop-off destination. Forexample, the delivery system identifies a particular publicinfrastructure terminal closest or near closest in distance to thedesired drop-off destination. In this example, the delivery systemdesignates the identified closest terminal as the terminal from whichthe drone will deliver the package. In some embodiments, once thedelivery system identifies the closest terminal, the delivery systemcommunicates an instruction to transport the package to the identifiedpublic infrastructure terminal (e.g., the delivery system sends amessage to a warehouse that has the package with an instruction totransport the package to the identified closest terminal).

In example embodiments, the delivery system receives an indication thatthe package has arrived at the identified public infrastructure terminal(e.g., a final terminal). In certain embodiments, the delivery systemreceives the indication that the package has arrived in response to aphysical identifier affixed to the package (e.g., a Radio FrequencyIdentification (RFID) tag, an optical barcode, or a smart tag) thatuniquely identifies the package being detected at the identified publicinfrastructure terminal. In other embodiments, the indication isreceived in response to an operator, administrator, or manager of thedelivery system inputting data into the delivery system for each packagethat is brought to the identified public infrastructure terminal.

In various embodiments, the delivery system determines a drone deliveryroute from the identified public infrastructure terminal to the drop-offdestination based on public infrastructure information (e.g., publictransportation infrastructure data including public infrastructureterminal locations and public infrastructure vehicle route information).In an example where drone delivery is facilitated by trains and trainroutes, the delivery system determines a drone delivery route by findinga minimum or near-minimum route distance between an identified traindepot and the drop-off destination. In further examples, the deliverysystem determines a three-dimensional delivery trajectory (e.g.,defining the longitude, latitude, and altitude along the route) to guidethe drone over power lines, above bridges, and around heavily forestedareas.

Once the delivery system determines a drone delivery route, in variousexample embodiments, the delivery system communicates, to the drone, aninstruction for the drone to deliver the package from the identifiedpublic infrastructure terminal to the drop-off destination using thedrone delivery route. Once the delivery system communicates aninstruction to the drone to deliver the package, in example embodiments,the drone picks up the package and flies along the determined dronedelivery route to drop off the package at the drop-off destination.

In one process arrival and unloading of a drone carrying pods at a droneoffload exchange station according to the embodiment of FIG. 4E. Theprocess is similar for both 1-pod and 4-pod drones. First, a dronecarrying content/pods arrives at a drone offload exchange station (202).At 204, the drone flies towards a first loading bay where it may dropoff one or more pods. For efficiency purposes, incoming drones mayunload pods in loading bays in ascending order according to slot numbersof their respective loading bay dock. In some cases, drones may drop offpods at a transit bay instead of a loading bay if a passenger in a podhas his exchange station as their final stop. In another case, if apassenger is flying to their final stop after the present exchangestation, the pod may be dropped off at a 1-pod drone bay to catch adrone to their final stop. At 206, the drone descends on the firstloading bay and unlatches from pods that are designated for drop-off atthe present loading bay. At step 208, after unloading of pods iscompleted at the present loading bay, the drone ascends to a safe flyingaltitude. At step 210, if the drone is still carrying pods, the processmay return to step 204 and repeat steps 204 to 210 for as many differentloading bays as necessary to drop off all pods. Once pod drop-off hasbeen completed, step 212 is reached, and a quick analysis is performedto decide whether the drone needs to be charged. If the power supply isat sufficient levels, the drone may be directed to park in a respectivedrone bay. In the case in which no other drone is available, the dronemay be directed by exchange station manage control to a loading bay topick up pods to fly to a next exchange station. From 212, if a charge isneeded, in 216 the drone may be directed to a charging bay, and docksinto an open spot to receive a charge.

FIG. 4F shows an exemplary process to use computer vision to deliver thepackage to a door or a designated spot:

Upload a flight plan to the flight control system of the vehicle and getauthorization (221)

Lift vehicle into the air in a vertical takeoff and landing mode (222)

Transition the vehicle from the vertical takeoff and landing mode to aforward flight mode (223)

Transport the vehicle to the desired destination location (224)

Use camera/computer vision to detect a door or a designated deliveryspot and navigate drone to the door/designated landing spot (225)

Transition vehicle from the forward flight mode to the vertical takeoffand landing mode (226)

Land vehicle at destination, deliver the package and return to base(227)

In one embodiment, the drone locks the content so thieves cannot takethe package, and only after the user signs on a web site or a mobile appor a smartwatch app to acknowledge delivery, and then the drone releasesthe package and returns to base.

In another embodiment, a buyer car trunk can be remotely opened and thedrone can use computer vision to drop the package into the car trunk,hover near the car, and notify the user to lock the trunk beforereturning to base.

Autonomous Flying Taxi Service

In one aspect, a flying vehicle includes: a cab with an optionalpassenger seat with optional steering control; a propulsion unit havinga rotating blade and an engine to rotate the blade; a rail from a cabtop extending toward one external side of the cab, the cab having amoveable actuator coupled to the propulsion unit to move the propulsionunit between a first position above the cab during lift-off and a secondposition during lateral (forward or backward) flight, the cab respondingto a taxi request to ferry a customer to a destination.

In another aspect, a navigation method includes receiving a servicerequest with a pickup location and a destination from a passenger;selecting a vehicle proximal to the passenger; picking up the passengerand transporting the passenger with the vehicle by: querying nearbyvehicles for nearby vehicle travel plans; generating a travel plan forthe vehicle based on the destination and the nearby vehicle travelplans; generating a travel path free of conflict and communicatingtravel plan to a traffic control system; executing the travel plan uponreceiving an approval from the traffic control system.

In yet another aspect shown in FIG. 5A, an exemplary method to providetaxi or on-demand transportation services includes:

-   -   receiving a service request with a pickup location and a        destination from a passenger;    -   selecting a vehicle proximal to the passenger;    -   picking up the passenger and transporting the passenger with the        vehicle by:        -   querying nearby vehicles for nearby vehicle travel plans;

generating a travel plan for the vehicle based on the destination andthe nearby vehicle travel plans;

generating a travel path free of conflict and communicating travel planto a traffic control system;

executing the travel plan upon receiving an approval from the trafficcontrol system.

Implementations can include one or more of the following sub operations:

-   -   The vehicle can resubmit the travel plan if rejected.

Implementations may include one or more of the following.

FIG. 5B shows an exemplary process for matching flying vehicles toloads. From the start 20, the process runs a load matching engine 22that receives posted vehicle data 24 and an active load list 26. Theload match engine checks if the vehicle meets load criteria 28 and ifnot the next vehicle is tested for a matching load. If the load criteriais met, the process checks if the vehicle is available in 30 and if notthe process loops back to 22 to process the next vehicle. If available,the process sends an available load list to the flight control system orvehicle owner web portal in 32. The available loads are displayed in34-36. The flying vehicle owner then reviews the load and calls the loadowner to negotiate price in 38. Next, if the load owner books thevehicle in 40, the load list is updated and vehicle is removed from theavailable list in 42 and the vehicle data is posted. Meanwhile, vehiclelocation is posted in the vehicle data 24 based on GPS and smartphonelocation transmissions in 44.

FIG. 5C shows an exemplary system to match shippers to flying vehicles,while FIG. 5B shows an exemplary system to match a flying vehicle withprospective customers. FIG. 5D shows an exemplary process to matchshippers to flying vehicles. The process includes determining one ormore flying vehicles proximal to a geographical location of a shippingload, each shipping load having a shipping profile (102). The processthen retrieves a profile of each nearby vehicle and comparing thevehicle profile with the shipping profile to identify one or morematching vehicles (104). The process then contacts matching vehicleflight control system 80(s) about the shipping load (106).

FIG. 5E shows a corresponding user interface for a flying vehicle 4looking for a customer. In FIG. 5B, a number of shipping prospects 6 aresent to the flying vehicle. The flying vehicle can retrieve eachprospect's desired load capacity, type of load, the start anddestination addresses, and desired delivery date and contact theprospect and/or provide a quote if there is a match.

FIG. 5F shows an exemplary process to match flying vehicles to shippers.The process includes tracking a geographical location of a mobile deviceassociated with a vehicle (110). The process determines one or moreshipping loads proximal to the geographical location of the mobiledevice, each shipping load having a shipping profile (112). The processalso includes retrieving a vehicle profile (114) and comparing thevehicle profile with each shipping profile to identify one or morematching loads (116). The process then messages a dispatching computeron the mobile device of matching loads (118). The process can includeratings by flying vehicles and shippers of their respective performance.Posting capacity can be done without the time consuming search ofvehicle boards. The information is centralized so that once done, theflying vehicle availability, profile, and capacity information can beviewed by a variety of uses and available much sooner to the freightowner. Once the freight owner sees the available capacity, he/she couldimmediately engage the capacity owner and negotiate for his/herservices. Thus, the capacity owner would not have to wait long for abooking and would be able to again be generating revenue from theengagement of the transportation asset. The system helps flying vehiclesfind perfect client ahead of time. Flying vehicles can build loyaltywith large shipper or freight brokerage who pays well and who needsservices regularly. The rating system allows flying vehicles andshippers to operate on the basis of quality and service rather thanbeing completely focused on getting the cheapest rate all the time.

Vehicle Sharing or Rental

In one aspect, a flying vehicle includes: a cab that can be rented byits owner to nearby passengers (network of passenger); a propulsion unithaving a rotating blade and an engine to rotate the blade; a rail from acab top extending toward one external side of the cab, the cab having amoveable actuator coupled to the propulsion unit to move the propulsionunit between a first position above the cab during lift-off and a secondposition during lateral (forward or backward) flight.

In another aspect, a method of sharing autonomous flying vehicleincludes: associating a unique identifier associated with a flightcontrol system with a sharing server; periodically analyzing a locationof the flight control system based on a geospatial data associated withthe location of the flight control system 80; listing the flight controlsystem on a sharing network; processing a payment of a renter of theflight control system in a threshold radial distance from the flightcontrol system when the flight control system is predictable at thenon-transitory location for a predictably available period of time;instructing the flight control system to navigate to the location of therenter; periodically updating the owner and the renter based on at leastone of a time in transit, time to arrival, time to destination, and apayment earned status.

Implementations may include one or more of the following. A company orindividual may own and/or lease a flight control system 10 as detailedbelow. In one aspect, a method of a flying vehicle sharing serverincludes associating a unique identifier associated with a flightcontrol system with the flying vehicle sharing server, periodicallyanalyzing a location of the flight control system based on a geospatialdata associated with the location of the flight control system 80, anddeclaring a non-transitory location of the flight control system basedon a predictable behavior algorithm. The method permits an owner of theflight control system to list the flight control system on a flyingvehicle sharing network. In addition, the method processes a payment ofa renter of the flight control system in a threshold radial distancefrom the flight control system when the flight control system ispredictable at the non-transitory location for a predictably availableperiod of time. Furthermore, a financial account of the owner of theflight control system is credited with the payment of the renter of theflight control system in the threshold radial distance from the flightcontrol system when the flight control system is predictable at thenon-transitory location for a predictably available period of time.

The unique identifier of the flight control system may be a licenseplate of the vehicle, and/or a social networking profile of the user ina geo-spatial social community. The method may include automaticallyrecommending connections to the owner of the flight control system basedon the non-transitory location. The connections may be associated withother users of the geo-spatial social community based on other users ofthe geo-spatial social community sharing a common interest with theowner in the threshold radial distance from the non-transitory location,and/or other flight control system of the geo-spatial social communitywhose owners share the common interest with the owner in the thresholdradial distance from the non-transitory location. The method may includeautomatically instructing the flight control system 80 car to navigateto the location of the renter, and/or periodically updating the ownerand/or the renter based on a time in transit, a time to arrival, time todestination, and/or the payment earned status. A criteria associatedwith a flying vehicle listing data including a description, aphotograph, a video, a rental fee, a category, a vehicle make, a vehiclemodel, and/or a functional status may be processed.

In addition, an availability chart may be populated when the flightcontrol system associated with the listing criteria is posted. Theavailability chart may include an operation area radius, a start timing,an end timing, an hours per day, and/or an hour per user. The method mayfurther include determining that the flying vehicle listing data isgenerated by the verified user of the neighborhood broadcast system whenvalidating that the flying vehicle listing data is associated with themobile device. It may be determined that an application on the mobiledevice is communicating the flying vehicle listing data to the flyingvehicle sharing network when the flying vehicle listing data may beprocessed.

The verified user may be associated with a verified user profile in theflying vehicle sharing network through the application on the mobiledevice. The flying vehicle listing data generated through the mobiledevice may be presented as a flying vehicle sharing alert pushpin of theflying vehicle listing data in a geospatial map surroundingpre-populated residential and/or business listings in a surroundingvicinity, such that the flying vehicle sharing alert pushpin of theflying vehicle listing data may automatically presented on thegeospatial map in addition to being presented on the set of userprofiles having associated verified addresses in the threshold radialdistance from the set of geospatial coordinates associated with theflying vehicle listing data generated through the mobile device of theverified user of the flying vehicle sharing server.

The flying vehicle listing data generated through the mobile device maybe radially distributed through an on-page posting, an electroniccommunication, and/or a push notification delivered to desktop and/ormobile devices associated with users and/or their user profiles aroundan epicenter defined at the set of geospatial coordinates associatedwith the flying vehicle listing data that may be generated through themobile device to all subscribed user profiles in a circular geo-fencedarea (defined by the threshold distance from the set of geospatialcoordinates associated with the flying vehicle listing data generatedthrough the mobile device) through the radial algorithm of the flyingvehicle sharing network that measures a distance away of each addressassociated with each user profile from the current geospatial locationat the epicenter.

The method may include permitting the verified user to drag and/or dropthe flying vehicle sharing alert pushpin on any location on thegeospatial map, and/or automatically determining a latitude and/orlongitude associated a placed location. The method may further includeautomatically notifying a user, a business, and/or a flying vehiclerental agency in a surrounding geospatial area to the set of geospatialcoordinates associated with the flying vehicle listing data generatedthrough the mobile device. The geospatial coordinates may be extractedfrom a metadata associated with the flying vehicle listing datagenerated through the mobile device when verifying that the set ofgeospatial coordinates associated with the flying vehicle listing datagenerated through the mobile device are trusted based on the claimedgeospatial location of the verified user of the flying vehicle sharingserver.

A relative match between a persistent clock associated with the flyingvehicle sharing server and/or a digital clock of the mobile device maybe determined to determine that the timestamp associated with thecreation date and/or time of the flying vehicle listing data generatedthrough the mobile device may accurate and/or therefore trusted. Apublishing of the flying vehicle listing data generated through themobile device may be automatically deleted on a set of user profileshaving associated verified addresses in the threshold radial distancefrom the set of geospatial coordinates associated with the flyingvehicle listing data generated through the mobile device of the verifieduser of the flying vehicle sharing server based on a flying vehiclesharing alert expiration time.

The method may also include geocoding a set of residential addresseseach of which may be associated with a resident name in a neighborhoodsurrounding the mobile device. The set of residential addresses eachassociated with the resident name may be pre-populated as the set ofuser profiles in the threshold radial distance from the claimedgeospatial location of the verified user of the flying vehicle sharingserver in a neighborhood curation system communicatively coupled withthe flying vehicle sharing server. The verified user may be permitted tomodify content in each of the set of user profiles. The modified contentmay be tracked through the neighborhood curation system. A reversiblehistory journal associated with each of the set of user profiles may begenerated such that a modification of the verified user can be undone ona modified user profile page.

An editing credibility of the verified user may be determined based onan edit history of the verified user and/or a community contributionvalidation of the verified user by other users of the neighborhoodcuration system. The method may include automatically publishing theflying vehicle listing data generated through the mobile device to a setof user profiles having associated verified addresses in a thresholdradial distance from the claimed geospatial location of the verifieduser of the flying vehicle sharing server using the radial algorithm.

A claim request of the verified user generating the flying vehiclelisting data generated through the mobile device to be associated withan address of the neighborhood curation system may be processed. It maybe determined if the claimable neighborhood in the neighborhood curationsystem may be associated with a private neighborhood community in theclaimable neighborhood of the neighborhood curation system. The verifieduser may be associated with the private neighborhood community in theclaimable neighborhood of the neighborhood curation system if theprivate neighborhood community has been activated by the verified userand/or a different verified user. The verified user may be permitted todraw a set of boundary lines in a form of a geospatial polygon such thatthe claimable neighborhood in a geospatial region surrounding the claimrequest may create the private neighborhood community in theneighborhood curation system if the private neighborhood community maybe inactive.

The method may verify the claim request of the verified user generatingthe flying vehicle listing data generated through the mobile device tobe associated with a neighborhood address of the neighborhood curationsystem when the address may be determined to be associated with a workaddress and/or a residential address of the verified user. The flyingvehicle listing data generated through the mobile device may besimultaneously published on the private neighborhood communityassociated with the verified user generating the flying vehicle listingdata generated through the mobile device in the threshold radialdistance from the address associated with the claim request of theverified user of the neighborhood curation system when automaticallypublishing the flying vehicle listing data generated through the mobiledevice on a set of user profiles having associated verified addresses ina threshold radial distance from the claimed geospatial location of theverified user of the flying vehicle sharing server based on a set ofpreferences of the verified user using the radial algorithm.

The renter and/or other renters may be permitted to view the ratingand/or the review provided by the flight control system 80 car owner foreach of the renters based on a participation criteria set by the flightcontrol system 80 car owner and/or the renter, such that each renter mayable to view ratings and/or reviews of each participating candidate forthe rental associated with the flying vehicle listing data. Each renterfor the rental of the flight control system associated with the flyingvehicle listing data may be permitted to communicate with each otherand/or form social connections with each other based on theparticipation criteria set by the flight control system 80 car ownerand/or the renter, such that each renter may able to form socialconnections with each participating candidate for the rental associatedwith the flying vehicle listing data.

The method may also include permitting participating flight controlsystem owners in the flying vehicle sharing server to see previousratings, comments, reviews, prescreen questions, and/or backgroundchecks of across a plurality of renters applying for a plurality flightcontrol system 80 car rentals through the flying vehicle sharing server(such that different flight control system 80 car owners benefit fromprevious diligence of at one of the previous ratings, comments, reviews,prescreen questions, and/or background checks by participating flightcontrol system 80 car owners with each renter that has previously rentedthrough the flying vehicle sharing server). A summary data may beprovided to the flight control system 80 car owner generating the flyingvehicle listing data generated through the mobile device of how manyuser profile pages were updated with an alert of the flying vehiclelisting data generated through the mobile device when publishing theflying vehicle listing data generated through the mobile device in theprivate neighborhood community and/or the set of user profiles havingassociated verified addresses in the threshold radial distance from theclaimed geospatial location of the verified user of the flying vehiclesharing server based on the set of preferences of the verified user.

The flying vehicle listing data generated through the mobile device maybe live broadcasted to the different verified user and/or other verifiedusers in the private neighborhood community (and/or currently within thethreshold radial distance from the current geospatial location) throughthe flying vehicle sharing server through a multicast algorithm suchthat a live broadcast multicasts to a plurality of data processingsystems associated with each of the different user and/or the otherverified users simultaneously (when the mobile device of the verifieduser generating the live-broadcast enables broadcasting of the flyingvehicle listing data generated through the mobile device to any one of ageospatial vicinity around the mobile device of the verified usergenerating the broadcast and/or in any private neighborhood community inwhich the verified user has a non-transitory connection). The differentverified user and/or other verified users in the private neighborhoodcommunity may be permitted to bi-directionally communicate with theverified user generating the broadcast through the flying vehiclesharing server.

Any private neighborhood community in which the verified user has anon-transitory connection may be a residential address of the verifieduser and/or a work address of the verified user that has been confirmedby the flying vehicle sharing server as being associated with theverified user. The threshold distance may be between 0.2 and/or 0.4miles from the set of geospatial coordinates associated with the flyingvehicle listing data generated through the mobile device to optimize arelevancy of the live-broadcast. The flying vehicle sharing server mayinclude a crowd-sourced moderation algorithm in which multiple neighborsin a geospatial area determine what content contributed to the flyingvehicle sharing server persists and/or which may be deleted.

The flying vehicle sharing server may permit users to mute messages ofspecific verified users to prevent misuse of the flying vehicle sharingserver. The flying vehicle sharing server may permit the flying vehiclelisting data to be disseminated to adjacent neighborhoods that have beenclaimed by different users in a manner such that the flying vehiclelisting data may optionally disseminated to the surrounding claimedneighborhoods based on a preference of the verified user. A claimedneighborhood of the verified user may be activated based on a minimumnumber of other verified users in the threshold radial distance thathave been verified through a primary residential address associated witheach of the other verified users through a postcard verification, autility bill verification, a privately-published access code, and/or aneighbor vouching method. Access to the flying vehicle listing data maybe restricted to the claimed neighborhood of the verified user. Accessto the flying vehicle listing data may denied to users having verifiedaddresses outside the claimed neighborhood of the verified user.

In another aspect, the method of the flight control system includescommunicating a unique identifier associated with the flight controlsystem with a flying vehicle sharing server and periodically determininga location of the flight control system based on a geospatial dataassociated with the location of the flight control system 80. The methodfurther includes automatically setting a navigation route of the flightcontrol system when the flight control system is located at anon-transitory location of the flight control system based on apredictable behavior algorithm. In addition, a payment of a renter ofthe flight control system in a threshold radial distance from the flightcontrol system is processed when the renter is picked up by the flightcontrol system 80. A unique identifier associated with a flight controlsystem may be associated with the flying vehicle sharing server. Alocation of the flight control system may be periodically analyzed basedon a geospatial data associated with the location of the flight controlsystem 80. A non-transitory location of the flight control system may bedeclared based on a predictable behavior algorithm. An owner of theflight control system may be permitted to list the flight control systemon a flying vehicle sharing network, wherein the flight control system80 car navigation route automatically instructed to navigate to thelocation of the renter.

In yet another aspect, a system includes a network and an autonomousvehicle to automatically set a navigation route of the autonomousvehicle to a location of a renter of the autonomous vehicle when theautonomous vehicle is located at a non-transitory location of theautonomous vehicle based on a predictable behavior algorithm. The systemalso includes a flying vehicle sharing server communicatively coupledwith the autonomous vehicle to credit a financial account of an owner ofthe autonomous vehicle with a payment of the renter of the autonomousvehicle in the threshold radial distance from the autonomous vehiclewhen the autonomous vehicle is predictable at the non-transitorylocation for a predictably available period of time.

A unique identifier associated with a flight control system may beassociated with the flying vehicle sharing server. A location of theflight control system may be periodically analyzed based on a geospatialdata associated with the location of the flight control system 80. Anon-transitory location of the flight control system may be declaredbased on a predictable behavior algorithm. An owner of the flightcontrol system may be permitted to list the flight control system on aflying vehicle sharing network, wherein the flight control system 80 carnavigation route automatically instructed to navigate to the location ofthe renter.

The unique identifier may be a license plate of the autonomous vehicle,and/or a social networking profile of the user in a geo-spatial socialcommunity. A connection recommendation module may automaticallyrecommend connections to the owner of the autonomous vehicle based onthe non-transitory location. The connections may be associated withother users of the geo-spatial social community based on other users ofthe geo-spatial social community sharing a common interest with theowner in the threshold radial distance from the non-transitory location,and/or other autonomous vehicles of the geo-spatial social communitywhose owners share a common interest with the owner in the thresholdradial distance from the non-transitory location. A navigation modulemay automatically instruct the autonomous vehicle to navigate to thelocation of the renter. An update module may periodically update theowner and/or the renter based on time in transit, time to arrival, timeto destination, and/or the payment earned status.

A criteria module may process a criteria associated with a flyingvehicle listing data including a description, a photograph, a video, arental fee, a category, a vehicle make, a vehicle model, and/or afunctional status. A charting module may populate an availability chartwhen the autonomous vehicle associated with the listing criteria isposted. The availability chart may include an operation area radius, astart timing, an end timing, an hours per day, and/or an hours per user.A validation module may determine that the flying vehicle listing datais generated by the verified user of the neighborhood broadcast systemwhen validating that the flying vehicle listing data is associated withthe mobile device. An application module may determine that anapplication on the mobile device is communicating the flying vehiclelisting data to the flying vehicle sharing network when the flyingvehicle listing data is processed.

An association module may associate the verified user with a verifieduser profile in the flying vehicle sharing network through theapplication on the mobile device. A pushpin module may present theflying vehicle listing data generated through the mobile device as aflying vehicle sharing alert pushpin of the flying vehicle listing datain a geospatial map surrounding pre-populated residential and/orbusiness listings in a surrounding vicinity (such that the flyingvehicle sharing alert pushpin of the flying vehicle listing data may beautomatically presented on the geospatial map in addition to beingpresented on the set of user profiles having associated verifiedaddresses in the threshold radial distance from the set of geospatialcoordinates associated with the flying vehicle listing data generatedthrough the mobile device of the verified user of the flying vehiclesharing server).

The flying vehicle listing data generated through the mobile device maybe radially distributed through an on-page posting, an electroniccommunication, and/or a push notification delivered to desktop and/ormobile devices associated with users and/or their user profiles aroundan epicenter defined at the set of geospatial coordinates associatedwith the flying vehicle listing data generated through the mobile deviceto all subscribed user profiles in a circular geo-fenced area (definedby the threshold distance from the set of geospatial coordinatesassociated with the flying vehicle listing data generated through themobile device) through the radial algorithm of the flying vehiclesharing network that may measure a distance away of each addressassociated with each user profile from the current geospatial locationat the epicenter. A placement module may permit the verified user todrag and/or drop the flying vehicle sharing alert pushpin on anylocation on the geospatial map, and/or automatically determine alatitude and/or a longitude associated a placed location. A notificationmodule may automatically notify a user, a business, and/or a flyingvehicle rental agency in a surrounding geospatial area to the set ofgeospatial coordinates associated with the flying vehicle listing datagenerated through the mobile device.

An extraction module may extract the geospatial coordinates from ametadata associated with the flying vehicle listing data generatedthrough the mobile device when verifying that the set of geospatialcoordinates associated with the flying vehicle listing data generatedthrough the mobile device are trusted based on the claimed geospatiallocation of the verified user of the flying vehicle sharing server. Amatching module may determine a relative match between a persistentclock associated with the flying vehicle sharing server and/or a digitalclock of the mobile device to determine that the timestamp associatedwith the creation date and/or time of the flying vehicle listing datagenerated through the mobile device may accurate and/or thereforetrusted. A deletion module may automatically delete a publishing of theflying vehicle listing data generated through the mobile device on a setof user profiles having associated verified addresses in the thresholdradial distance from the set of geospatial coordinates associated withthe flying vehicle listing data generated through the mobile device ofthe verified user of the flying vehicle sharing server based on a flyingvehicle sharing alert expiration time.

A plotting module may geocode a set of residential addresses eachassociated with a resident name in a neighborhood surrounding the mobiledevice. A data-seeding module may prepopulate the set of residentialaddresses each associated with the resident name as the set of userprofiles in the threshold radial distance from the claimed geospatiallocation of the verified user of the flying vehicle sharing server in aneighborhood curation system communicatively coupled with the flyingvehicle sharing server. A modification module may permit the verifieduser to modify content in each of the set of user profiles. A discoverymodule may track the modified content through the neighborhood curationsystem. An undo module may generate a reversible history journalassociated with each of the set of user profiles such that amodification of the verified user can be undone on a modified userprofile page. A reputation module may determine an editing credibilityof the verified user based on an edit history of the verified userand/or a community contribution validation of the verified user by otherusers of the neighborhood curation system. A publication module mayautomatically publish the flying vehicle listing data generated throughthe mobile device to a set of user profiles having associated verifiedaddresses in a threshold radial distance from the claimed geospatiallocation of the verified user of the flying vehicle sharing server usingthe radial algorithm.

A claiming module may process a claim request of the verified usergenerating the flying vehicle listing data generated through the mobiledevice to be associated with an address of the neighborhood curationsystem. A private-neighborhood module may determine if the claimableneighborhood in the neighborhood curation system may be associated witha private neighborhood community in the claimable neighborhood of theneighborhood curation system. An association module may associate theverified user with the private neighborhood community in the claimableneighborhood of the neighborhood curation system if the privateneighborhood community has been activated by the verified user and/or adifferent verified user. A boundary module may permit the verified userto draw a set of boundary lines in a form of a geospatial polygon suchthat the claimable neighborhood in a geospatial region surrounding theclaim request may create the private neighborhood community in theneighborhood curation system if the private neighborhood community mayinactive.

An address type module may verify the claim request of the verified usergenerating the flying vehicle listing data generated through the mobiledevice to be associated with a neighborhood address of the neighborhoodcuration system when the address is determined to be associated with awork address and/or a residential address of the verified user. Aconcurrency module may simultaneously publish the flying vehicle listingdata generated through the mobile device on the private neighborhoodcommunity associated with the verified user generating the flyingvehicle listing data generated through the mobile device in thethreshold radial distance from the address associated with the claimrequest of the verified user of the neighborhood curation system (whenautomatically publishing the flying vehicle listing data generatedthrough the mobile device on a set of user profiles having associatedverified addresses in a threshold radial distance from the claimedgeospatial location of the verified user of the flying vehicle sharingserver based on a set of preferences of the verified user using theradial algorithm).

A communication module may automatically initiate a video communicationand/or an audio communication between the mobile device of the owner ofthe autonomous vehicle and/or another mobile device of the renterthrough the flying vehicle sharing server based on the profile of therenter associated with the flying vehicle listing data through theflying vehicle sharing server. A review module may permit the renterand/or other renters to view the rating and/or the review provided bythe owner of the autonomous vehicle for each of the renters based on aparticipation criteria set by the owner of the autonomous vehicle and/orthe renter, such that each renter may be able to view ratings and/orreviews of each participating candidate for the rental associated withthe flying vehicle listing data. A social connection module may permiteach renter for the rental of the autonomous vehicle associated with theflying vehicle listing data to communicate with each other and/or formsocial connections with each other based on the participation criteriaset by the owner of the autonomous vehicle and/or the renter, such thateach renter may able to form social connections with each participatingcandidate for the rental associated with the flying vehicle listingdata.

A diligence module may permit participating owners of the autonomousvehicles in the flying vehicle sharing server to see previous ratings,comments, reviews, prescreen questions, and/or background checks ofacross a plurality of renters applying for a plurality autonomousvehicle rentals through the flying vehicle sharing server such thatdifferent owner of the autonomous vehicles benefit from previousdiligence of at one of previous ratings, comments, reviews, prescreenquestions, and/or background checks by participating owner of theautonomous vehicles with each renter that has previously rented throughthe flying vehicle sharing server. A summary module may provide asummary data to the owner of the autonomous vehicle generating theflying vehicle listing data generated through the mobile device of howmany user profile pages were updated with an alert of the flying vehiclelisting data generated through the mobile device when publishing theflying vehicle listing data generated through the mobile device in theprivate neighborhood community and/or the set of user profiles havingassociated verified addresses in the threshold radial distance from theclaimed geospatial location of the verified user of the flying vehiclesharing server based on the set of preferences of the verified user.

A live broadcast module may live broadcast the flying vehicle listingdata generated through the mobile device to the different verified userand/or other verified users in the private neighborhood community and/orcurrently within the threshold radial distance from the currentgeospatial location through the flying vehicle sharing server through amulticast algorithm such that a live broadcast multicasts to a pluralityof data processing systems associated with each of the different userand/or the other verified users simultaneously (when the mobile deviceof the verified user generating the live-broadcast enables broadcastingof the flying vehicle listing data generated through the mobile deviceto any one of a geospatial vicinity around the mobile device of theverified user generating the broadcast and/or in any privateneighborhood community in which the verified user has a non-transitoryconnection).

A bi-directional communication module may permit the different verifieduser and/or other verified users in the private neighborhood communityto bi-directionally communicate with the verified user generating thebroadcast through the flying vehicle sharing server. Any privateneighborhood community in which the verified user has a non-transitoryconnection may be a residential address of the verified user and/or awork address of the verified user that has been confirmed by the flyingvehicle sharing server as being associated with the verified user. Thethreshold distance may be between 0.2 and/or 0.4 miles from the set ofgeospatial coordinates associated with the flying vehicle listing datagenerated through the mobile device to optimize a relevancy of thelive-broadcast. The flying vehicle sharing server may include acrowd-sourced moderation algorithm in which multiple neighbors in ageospatial area may determine what content contributed to the flyingvehicle sharing server persists and/or which may be deleted. The flyingvehicle sharing server may permit users to mute messages of specificverified users to prevent misuse of the flying vehicle sharing server.

The flying vehicle sharing server may permit the flying vehicle listingdata to be disseminated to adjacent neighborhoods that have been claimedby different users in a manner such that the flying vehicle listing datamay be optionally disseminated to the surrounding claimed neighborhoodsbased on a preference of the verified user. A claimed neighborhood ofthe verified user may be activated based on a minimum number of otherverified users in the threshold radial distance that have been verifiedthrough a primary residential address associated with each of the otherverified users through a postcard verification, a utility billverification, a privately-published access code, and/or a neighborvouching system. Access to the flying vehicle listing data may berestricted to the claimed neighborhood of the verified user. Access tothe flying vehicle listing data may be denied to users having verifiedaddresses outside the claimed neighborhood of the verified user.

Implementations may include the following actions: populating anavailability chart when the flight control system associated with alisting criteria is posted, wherein the availability chart includes atleast one of an operation area radius, a start timing, an end timing, anhours per day, an hours per user; determining that the automotivelisting data is generated by a verified user of a neighborhood broadcastsystem when validating that the automotive listing data is associatedwith a mobile device; determining that an application on the mobiledevice is communicating the automotive listing data to the automobilesharing network when the automotive listing data is processed;associating the verified user with a verified user profile in theautomobile sharing network through the application on the mobile device;and presenting the automotive listing data generated through the mobiledevice as an automobile sharing alert pushpin of the automotive listingdata in a geospatial map surrounding pre-populated residential andbusiness listings in a surrounding vicinity, such that the automobilesharing alert pushpin of the automotive listing data is automaticallypresented on a geospatial map in addition to being presented on a set ofuser profiles having associated verified addresses in the thresholdradial distance from a set of geospatial coordinates associated with theautomotive listing data generated through the mobile device of theverified user of the automobile sharing server; wherein the automotivelisting data generated through the mobile device is radially distributedthrough at least one of an on-page posting, an electronic communication,and a push notification delivered to desktop and mobile devicesassociated with users and their user profiles around an epicenterdefined at the set of geo spatial coordinates associated with theautomotive listing data generated through the mobile device to allsubscribed user profiles in a circular geo-fenced area defined by athreshold distance from the set of geospatial coordinates associatedwith the automotive listing data generated through the mobile devicethrough a radial algorithm of the automobile sharing network thatmeasures a distance away of each address associated with each userprofile from a current geospatial location at the epicenter. The uniqueidentifier of the flight control system is at least one of a licenseplate of the flight control system 80, and a social networking profileof a user in a geo-spatial social community. Other actions includeautomatically recommending connections to the owner of the flightcontrol system based on the non-transitory location, wherein a set ofconnections are associated with other users of the geo-spatial socialcommunity based on at least one of: other users of the geo-spatialsocial community sharing a common interest with the owner in thethreshold radial distance from the non-transitory location, and otherflight control system 80 of the geo-spatial social community whoseowners share a common interest with the owner in the threshold radialdistance from the non-transitory location. One implementation includesprocessing a claim request of the verified user generating theautomotive listing data generated through the mobile device to beassociated with an address of the neighborhood curation system;determining if a claimable neighborhood in the neighborhood curationsystem is associated with a private neighborhood community in theclaimable neighborhood of the neighborhood curation system; associatingthe verified user with the private neighborhood community in theclaimable neighborhood of the neighborhood curation system if theprivate neighborhood community has been activated by at least one of theverified user and a different verified user; permitting the verifieduser to draw a set of boundary lines in a form of a geospatial polygonsuch that the claimable neighborhood in a geospatial region surroundingthe claim request creates the private neighborhood community in theneighborhood curation system if the private neighborhood community isinactive; verifying the claim request of the verified user generatingthe automotive listing data generated through the mobile device to beassociated with a neighborhood address of the neighborhood curationsystem when the address is determined to be associated with at least oneof a work address and a residential address of the verified user; andsimultaneously publishing the automotive listing data generated throughthe mobile device on the private neighborhood community associated withthe verified user generating the automotive listing data generatedthrough the mobile device in the threshold radial distance from theaddress associated with the claim request of the verified user of theneighborhood curation system when automatically publishing theautomotive listing data generated through the mobile device on the setof user profiles having associated verified addresses in the thresholdradial distance from the claimed geospatial location of the verifieduser of the automobile sharing server based on a set of preferences ofthe verified user using the radial algorithm. Another implementationincludes automatically downloading a set of profiles to the mobiledevice, wherein the owner of the flight control system is the verifieduser; automatically initiating at least one of a video communication andan audio communication between the mobile device of the owner of theflight control system and another mobile device the renter through theautomobile sharing server based on the profile of the renter associatedwith the automotive listing data through the automobile sharing server;permitting the renter and other renters to view at least one of therating and the review provided by the owner of the flight control systemfor each of the renters based on a participation criteria set by atleast one of the owner of the flight control system and the renter, suchthat each renter is able to view ratings and reviews of eachparticipating candidate for a rental associated with the automotivelisting data; permitting each renter for the rental of the flightcontrol system associated with the automotive listing data tocommunicate with each other and form social connections with each otherbased on the participation criteria set by at least one of the owner ofthe flight control system and the renter, such that each renter is ableto form social connections with each participating candidate for therental associated with the automotive listing data; permittingparticipating owner of the flight control system 80 in the automobilesharing server to see at least one of previous ratings, comments,reviews, prescreen questions, and background checks of across aplurality of renters applying for a plurality flight control systemrentals through the automobile sharing server such that different ownersof the flight control system 80 benefit from previous diligence of atone of the previous ratings, comments, reviews, prescreen questions, andbackground checks by participating owner of the flight control system 80with each renter that has previously rented through the automobilesharing server; and providing a summary data to the owner of the flightcontrol system generating the automotive listing data generated throughthe mobile device of how many user profile pages were updated with analert of the automotive listing data generated through the mobile devicewhen publishing the automotive listing data generated through the mobiledevice in at least one of the private neighborhood community and the setof user profiles having associated verified addresses in the thresholdradial distance from the claimed geospatial location of the verifieduser of the automobile sharing server based on the set of preferences ofthe verified user. Yet other implementation includes live broadcastingthe automotive listing data generated through the mobile device to thedifferent verified user and other verified users in at least one of theprivate neighborhood community and currently within the threshold radialdistance from a current geospatial location through the automobilesharing server through a multicast algorithm such that a live broadcastmulticasts to a plurality of data processing systems associated witheach of a different user and the other verified users simultaneouslywhen the mobile device of the verified user generating thelive-broadcast enables broadcasting of the automotive listing datagenerated through the mobile device to any one of a geospatial vicinityaround the mobile device of the verified user generating a broadcast andin any private neighborhood community in which the verified user has anon-transitory connection; and permitting the different verified userand other verified users in at least one of the private neighborhoodcommunity to bi-directionally communicate with the verified usergenerating the broadcast through the automobile sharing server, whereinany private neighborhood community in which the verified user has thenon-transitory connection is at least one of the residential address ofthe verified user and the work address of the verified user that hasbeen confirmed by the automobile sharing server as being associated withthe verified user, wherein a threshold distance is between 0.2 and 0.4miles from the set of geospatial coordinates associated with theautomotive listing data generated through the mobile device to optimizea relevancy of the live-broadcast, wherein the automobile sharing serverincludes a crowd-sourced moderation algorithm in which multipleneighbors to a geospatial area determine what content contributed to theautomobile sharing server persists and which is deleted, wherein theautomobile sharing server permits users to mute messages of specificverified users to prevent misuse of the automobile sharing server,wherein the automobile sharing server permits the automotive listingdata to be disseminated to adjacent neighborhoods that have been claimedby different users in a manner such that the automotive listing data isoptionally disseminated to the surrounding claimed neighborhoods basedon a preference of the verified user, wherein a claimed neighborhood ofthe verified user is activated based on a minimum number of otherverified users in the threshold radial distance that have been verifiedthrough a primary residential address associated with each of the otherverified users through at least one of a post card verification, autility bill verification, a privately-published access code, and aneighbor vouching method, wherein access to the automotive listing datais restricted to the claimed neighborhood of the verified user, andwherein access to the automotive listing data is denied to users havingverified addresses outside the claimed neighborhood of the verifieduser.

Edge Computing with Low Latency

In one aspect, a flying vehicle includes: a cab with a flight controlunit communicating with processors in the edge (ATC through edgecomputing); a propulsion unit having a rotating blade and an engine torotate the blade; a rail from a cab top extending toward one externalside of the cab, the cab having a moveable actuator coupled to thepropulsion unit to move the propulsion unit between a first positionabove the cab during lift-off and a second position during lateral(forward or backward) flight.

Implementations may include one or more of the following. The ATC 89preferably uses edge computing, preferably a low latency high speednetwork such as ultrawideband network, WiMAx, or 5G/6G networks. Forexample, an edge computing “Intelligent Edge Network,” or iEN runs on aninternal cloud network with virtualized network functions that aremanaged by a virtual infrastructure manager. Multi-access edge computingsoftware run on servers in one of the operator's C-RAN hubs withAI-powered object recognition software. The latency speeds obtainedthrough the company's edge computing can be from 15-5 ms. The systemremotely send the right software to the correct C-RAN or SAP computinglocation to provide a localized service. The only way to reduce latencyspeeds is to ensure that computing requests are crunched geographicallyclose to the user and removing remote hops is the key to loweringnetwork latency.

In yet another aspect, a system includes a cellular transceiver tocommunicate with a predetermined target; one or more antennas coupled tothe 5G transceiver each electrically or mechanically steerable to thepredetermined target; a processor to control a directionality of the oneor more antennas in communication with the predetermined target; and anedge processing module coupled to the processor and the one or moreantennas to provide low-latency computation for the predeterminedtarget.

Implementations can include one or more of the following. The processorcalibrates a radio link between a transceiver in the housing and aclient device. The processor is coupled to fiber optics cable tocommunicate with a cloud-based radio access network (RAN) or a remoteRAN. The processor calibrates a connection by analyzing RSSI and TSSIand moves the antennas until predetermined cellular parameters arereached. The edge processing module comprises at least a processor, agraphical processing unit (GPU), a neural network, a statistical engine,or a programmable logic device (PLD). The edge processing module isembedded in the antenna housing. The edge processing module can be apole, a building, or a light. The cellular transceiver can be a 5Gtransceiver. The processor coordinates beam sweeping by the one or moreantennas with radio nodes or user equipment (UE) devices based uponservice level agreement, performance requirement, traffic distributiondata, networking requirements or prior beam sweeping history. The beamsweeping is directed at a group of autonomous vehicles or a group ofvirtual reality devices. A neural network coupled to a control plane, amanagement plane, and a data plane to optimize 5G parameters. One ormore cameras and sensors in the housing to capture security information.Edge sensors mounted on the housing of the antenna can include LIDAR andRADAR. A camera can perform individual identity identification. The edgeprocessing module streams data to the predetermined target to minimizeloading the target. The edge processing module shares workload with acore processing module located at a head-end and a cloud module locatedat a cloud data center, each processing module having increased latencyand each having a processor, a graphical processing unit (GPU), a neuralnetwork, a statistical engine, or a programmable logic device (PLD). Anedge learning machine in the housing to provide local edge processingfor Internet-of-Things (IOT) sensors with reduced off-chip memoryaccess. The edge learning machine uses pre-trained models and modifiesthe pre-trained models for a selected task. A cellular device for aperson crossing a street near a city light or street light can emit aperson to vehicle (P2V) or a vehicle to person (V2P) safety message. Acloud trained neural network whose network parameters are down-sampledand filter count reduced before transferring to the edge neural network.

In yet another aspect, a system includes: a cellular transceiver tocommunicate with a predetermined target including a flying vehicle; oneor more antennas coupled to the 5G transceiver each electrically ormechanically steerable to the predetermined target; a processor tocontrol a directionality of the one or more antennas in communicationwith the predetermined target; and an edge processing module coupled tothe processor and the one or more antennas to provide low-latencycomputation for the predetermined target.

Implementations may include one or more of the following. The processorcalibrates a radio link between a transceiver in the housing and aclient device. The processor is coupled to fiber optics cable tocommunicate with a cloud-based radio access network (RAN) or a remoteRAN. The processor calibrates a connection by analyzing RSSI and TSSIand moves the antennas until predetermined cellular parameters arereached. The edge processing module comprises at least a processor, agraphical processing unit (GPU), a neural network, a statistical engine,or a programmable logic device (PLD). The edge processing module and theantenna comprise one unit. The unit comprises a pole, a building, or alight. The cellular transceiver comprises a 5G transceiver. Theprocessor coordinates beam sweeping by the one or more antennas withradio nodes or user equipment (UE) devices based upon service levelagreement, performance requirement, traffic distribution data,networking requirements or prior beam sweeping history. The beamsweeping is directed at a group of autonomous vehicles or a group ofvirtual reality devices. A neural network coupled to a control plane, amanagement plane, and a data plane to optimize 5G parameters. One ormore cameras and sensors in the housing can capture securityinformation. Ege sensors including LIDAR and RADAR. The edge processingmodule streams data to the predetermined target to minimize loading thetarget. The edge processing module shares workload with a coreprocessing module located at a head-end and a cloud module located at acloud data center, each processing module having increased latency andeach having a processor, a graphical processing unit (GPU), a neuralnetwork, a statistical engine, or a programmable logic device (PLD).

Beam Sweeping with Low Latency

In one aspect, a system includes a cellular transceiver to communicatewith a predetermined target including a flying vehicle; one or moreantennas coupled to the 5G transceiver each electrically steerable tothe predetermined target; and a processor to generate an antenna beamsweeping command based upon traffic distribution data, device networkingrequirements or a prior beam sweeping history to focus at least one beamfor communication with the predetermined target.

In another aspect, a flying vehicle includes: a cab with an optionalpassenger seat with optional steering control; a propulsion unit havinga rotating blade and an engine to rotate the blade; a rail from a cabtop extending toward one external side of the cab, the cab having amoveable actuator coupled to the propulsion unit to move the propulsionunit between a first position above the cab during lift-off and a secondposition during lateral (forward or backward) flight.

Implementations can include one or more of the following. The trafficdistribution data may be generated by collecting traffic data from radionodes and/or UE devices, or the networking requirements may include, forexample, service requirements associated with one or more applications(vehicular or reality application) on one or more UE devices. Theprocessor calibrates a radio link between a transceiver in the housingand a client device. The processor is coupled to fiber optics cable tocommunicate with a cloud-based radio access network (RAN) or a remoteRAN. The processor calibrates a connection by analyzing RSSI and TSSIand moves the antennas until predetermined cellular parameters arereached. The cellular transceiver can be a 5G transceiver. The processorcoordinates beam sweeping by the one or more antennas with radio nodesor user equipment (UE) devices based upon service level agreement,performance requirement, traffic distribution data, networkingrequirements or prior beam sweeping history. The beam sweeping isdirected at a group of autonomous vehicles or a group of virtual realitydevices. A neural network (NN) can be used to determine the beamsweeping. The NN can be connected to a control plane, a managementplane, and a data plane to optimize 5G parameters. One or more camerasand sensors in the housing can capture security information. Edgesensors mounted on the housing of the antenna can include LIDAR andRADAR and such data can be sent at top priority to vehicles passing by.A camera can perform individual identity identification. The edgeprocessing module streams data to the predetermined target to minimizeloading the target. The edge processing module shares workload with acore processing module located at a head-end and a cloud module locatedat a cloud data center, each processing module having increased latencyand each having a processor, a graphical processing unit (GPU), a neuralnetwork, a statistical engine, or a programmable logic device (PLD). Anedge learning machine in the housing to provide local edge processingfor Internet-of-Things (MT) sensors with reduced off-chip memory access.The edge learning machine uses pre-trained models and modifies thepre-trained models for a selected task. A cellular device for a personcrossing a street near a city light or street light can emit a person tovehicle (P2V) or a vehicle to person (V2P) safety message. A cloudtrained neural network whose network parameters are down-sampled andfilter count reduced before transferring to the edge neural network. Anedge processing module can be connected to the processor and the one ormore antennas to provide low-latency computation for the predeterminedtarget. The edge processing module can be at least a processor, agraphical processing unit (GPU), a neural network, a statistical engine,or a programmable logic device (PLD). The edge processing module isembedded in the antenna housing. The edge processing module can be partof a pole, a building, or a light. The processor can run code includingrequesting a portion of a network for a group of devices, checking foravailable resources to satisfy the request and assigning a network slicedeployment layout satisfying the requested portion of the networkincluding antenna level layout, and managing resources at the antennalevel as part of the requested portion of the network to providecommunication for the group. The request can be for enhanced servicesfor autonomous vehicles. The request can be for reality applicationssuch as virtual reality or augmented reality. Upon request, the systemdetermines a candidate network slice deployment layout that satisfiesthe network level requirements and network layouts of a service requestand costs associated with a candidate network resource and/or acandidate network slice deployment layout, which is used as a basis tooptimally use available network resources. The system coordinates,authorizes, releases and/or engages network resources in network. Thesystem obtains network slice deployment layout descriptors correspondingto a network slice deployment layout and the system may manage theprovisioning of the network slice deployment layout to satisfy theservice request. The system may perform various functions such as, forexample, network slice life cycle management, configuration management(e.g., policies, isolation of management), performance management (e.g.,service level agreement (SLA) management, service assurance andprogrammability), service mapping. The system updates in real timenetwork resource device regarding availability of network resourcesbased on the current state of network resources in network andprovisioned network resources that support service requests. The systemcan respond to the request with a monetary cost associated with acandidate network slice deployment layout, quality-of-service valuesassociated with the candidate network slice deployment layout (e.g.,minimum value and/or maximum value pertaining to latency, bandwidth,reliability, etc.) and/or other information representative of theconfiguration and/or service such as virtual network resource,non-virtual network resource, cloud, 5G RAN access, for example. Themethod includes storing network resource and capability informationpertaining to network resources of a network and generating networklevel requirement information that would support the network service.The method includes creating end-to-end network slice deploymentinformation that includes parameters to provision an end-to-end networkslice deployment layout in the network that supports the networkservice.

Network Slicing for Groups of Devices

In one aspect, a flying vehicle is part of a vehicular network, thevehicle having a cab with a flight control system that communicatesthrough dedicated private wireless services aside only for members ofthe vehicular network; a propulsion unit having a rotating blade and anengine to rotate the blade; a rail from a cab top extending toward oneexternal side of the cab, the cab having a moveable actuator coupled tothe propulsion unit to move the propulsion unit between a first positionabove the cab during lift-off and a second position during lateral(forward or backward) flight.

A method to manage a cellular network includes requesting a portion of anetwork for a group of flying vehicles, checking for available resourcesto satisfy the request and assigning a network slice deployment layoutsatisfying the requested portion of the network including antenna levellayout, and managing resources at the antenna level as part of therequested portion of the network to provide communication for the group.

In one implementation, the request can be for enhanced services forautonomous vehicles. The request can be for reality applications such asvirtual reality or augmented reality. Upon request, the systemdetermines a candidate network slice deployment layout that satisfiesthe network level requirements and network layouts of a service requestand costs associated with a candidate network resource and/or acandidate network slice deployment layout, which is used as a basis tooptimally use available network resources. The system coordinates,authorizes, releases and/or engages network resources in network. Thesystem obtains network slice deployment layout descriptors correspondingto a network slice deployment layout and the system may manage theprovisioning of the network slice deployment layout to satisfy theservice request. The system may perform various functions such as, forexample, network slice life cycle management, configuration management(e.g., policies, isolation of management), performance management (e.g.,service level agreement (SLA) management, service assurance andprogrammability), service mapping. The system updates in real timenetwork resource device regarding availability of network resourcesbased on the current state of network resources in network andprovisioned network resources that support service requests. The systemcan respond to the request with a monetary cost associated with acandidate network slice deployment layout, quality-of-service valuesassociated with the candidate network slice deployment layout (e.g.,minimum value and/or maximum value pertaining to latency, bandwidth,reliability, etc.) and/or other information representative of theconfiguration and/or service such as virtual network resource,non-virtual network resource, cloud, 5G RAN access, for example. Themethod includes storing network resource and capability informationpertaining to network resources of a network and generating networklevel requirement information that would support the network service.The method includes creating end-to-end network slice deploymentinformation that includes parameters to provision an end-to-end networkslice deployment layout in the network that supports the networkservice.

Learning System Plane

In one aspect, a flying vehicle includes: a cab; a propulsion unithaving a rotating blade and an engine to rotate the blade; and a 5Gnetwork with a neural network to optimize computation and transmission.

In another aspect, a system to optimize data flow in a 5G network,includes a neural network plane; a control plane coupled to the neuralnetwork plane; a management plane coupled to the neural network plane; adata plane coupled to the neural network plane, wherein the neuralnetwork plane receives cellular network statistics from the data planefor training, and during run-time, the neural network provides operatingparameters to the data, control and management planes; and one or moreoperations sending resource request to the neural network plane forautonomous air vehicle control.

Implementations of the system may include one or more of the following:A moveable surface; and one or more antennas mounted on the moveablesurface to change a direction of the antenna to a predetermined target.A pneumatic actuator or electrical motor can be placed under processorcontrol to change the curvature of the lens and to change thedirectionality of the antenna. The processor can calibrate the RF linkbetween the tower and the client device. The processor can calibrate theconnection by examining the RSSI and TSSI and scan the moveable surfaceuntil the optimal RSSI/TSSI levels (or other cellular parameters) arereached. The scanning of the target device can be done by moving theactuators up or down. Opposing actuator arrays can be formed to providetwo-sided communication antennas. An array of actuators can be used(similar to bee eyes), each antenna is independently steerable tooptimize 5G transmission. Fresnel lens can be used to improve SNR. Focusthe antenna on BS and UE, and then combine antennas for orthogonaltransmissions based on various factors. The focusing of the actuatorscan be automatically done using processor with iterative changes in theorientation of the antenna by changing the actuators until predeterminedcriteria is achieved such as the best transmission speed, TSSI, RSSI,SNR, among others. This is similar to the way human vision eyeglasscorrection is done.

FIG. 6 shows an exemplary simplified massive MIMO system with antennaports for user streams associated with flight control. Each user streamis a spatial stream of data. Each spatial stream that may include datafrom multiple users that are allocated different frequencies within thesame spatial stream, in some embodiments. Further, a given user may beallocated multiple spatial streams, in some embodiments. Therefore, thenumber of users communicating with the system may or may not correspondto the number of antenna ports. In some embodiments, MIMO RX isconfigured to perform the functionality of channel estimator, MIMOdetector, link quality evaluator, etc. In some embodiments, MIMO TX isconfigured to perform MIMO precoder.

During operation, a base station selects a number of antennas from amonga plurality of available antennas for use in MIMO wirelesscommunications. For example, the system may include 128 antennas but thebase station may select to use only 64 antennas during a given timeinterval based on current operating conditions. The decision of how manyantennas to use may be based on user input, a number of users currentlyin a cell, wireless signal conditions, bandwidth of currentcommunications, desired testing conditions, etc. The base station mayselect different numbers of antennas at different times, e.g., a largernumber during peak communications intervals and a smaller number duringtrough intervals. The base station determines a number of processingelements for processing received signals from the selected number ofantennas. In the illustrated embodiment, this is based on the number ofantennas selected and one or more threshold throughput values. In someembodiments, this determination may be based on any of variousappropriate parameters in addition to and/or in place of the parameters,including without limitation: the processing capacity of each processingelement, the amount of data per sample or entry for various information,a sampling rate, the number of spatial streams, number of users, etc.Determining the number of processing elements may include determining anumber of parallel receive chains for MIMO RX. In some embodiments, eachreceive chain includes a configurable MIMO core and a configurablelinear decoder. The base station processes incoming wirelesscommunications using the determined number of processing elements. Thismay include applying a MIMO signal estimation techniques such as MMSE,ZF, or MRC and decoding received data streams. After processing, thedecoded data from the determined number of processing elements may bereformatted and routed and transmitted to appropriate destinations(e.g., via another network such as a carrier network, the Internet,etc.). In some embodiments, the base station dynamically switchesbetween different MIMO signal estimation techniques, e.g., based on userinput, operating conditions, or any of various appropriate parameters.

The neural network control of the MIMO system may, in some embodiments,facilitate testing of MIMO base stations, reduce power consumptionduring MIMO communications, allow for flexibility in capacity, allow forflexibility in MIMO signal estimation, allow routing around defectiveprocessing elements or antennas, etc. In some embodiments, the basestation may also be dynamically or statically customized for a widevariety of operating conditions and/or research needs and may beconfigured for real-time processing.

FIG. 7A shows an exemplary 5G control system that uses learning machinesor neural networks to improve performance. The neural network planeprovides automated intelligence to select the best operations givenparticular mobile device or wireless client needs. By enabling bothclient and infrastructure intelligence, the 5G networked system couldreason about the deficiencies it suffers from, and improve itsreliability, performance and security. By pushing more network knowledgeand functions to the end host, the 5G clients could play more activeroles in improving the user-experienced reliability, performance andsecurity. The neural plane sits above the data plane, control plane andmanagement plane. The Control Plane makes decisions about how to set upthe antenna settings and where traffic is sent. Control plane packetsare destined to or locally originated by the router itself. The controlplane functions include the system configuration, management, andexchange of routing table information. The route controller exchangesthe topology information with other routers and constructs a routingtable based on a routing protocol, for example, RIP, OSPF or BGP.Control plane packets are processed by the router to update the routingtable information. It is the signaling of the network. Since the controlfunctions are not performed on each arriving individual packet, they donot have a strict speed constraint and are less time-critical. The DataPlane or Forwarding Plane Forwards traffic to the next hop along thepath to the selected destination network according to control planelogic. Data plane packets go through the router. The routers/switchesuse what the control plane built to dispose of incoming and outgoingframes and packets. The management plane configures, monitors, andprovides management, monitoring and configuration services to, alllayers of the network stack and other parts of the system. It should bedistinguished from the control plane, which is primarily concerned withrouting table and forwarding information base computation.

On the client side, the system collect runtime, fine-grained information(protocol states, parameters, operation logic, etc.) from full-stackcellular protocols (physical/link layer, radio resource control,mobility management, data session management) inside the 5G device orphone, and such information is provided to the neural network plane. Oneembodiment extracts cellular operations from signaling messages betweenthe device and the network. These control-plane messages regulateessential utility functions of radio access, mobility management,security, data/voice service quality, to name a few. Given thesemessages, it further enables in-device analytics for cellular protocols.The system infers runtime protocol state machines and dynamics on thedevice side, but also infer protocol operation logic (e.g., handoffpolicy from the carrier) from the network. The system collects rawcellular logs from the cellular interface to the device user-space atruntime, and then parses them into protocol messages and extracts theircarried information elements. The parsed messages are then fed to theanalyzer which aims to unveil protocol dynamics and operation logics.Based on the observed messages and the anticipated behavior model (fromcellular domain knowledge), the analyzer infers protocol states,triggering conditions for state transitions, and protocol's takenactions. Moreover, it infers certain protocol operation logic (e.g.,handoff) that uses operator-defined policies and configurations. Itoffers built-in abstraction per protocol and allows for customize theseanalyzers. On the management plane, the system captures full-stacknetwork information on all-layer operations (from physical to datasession layer) over time and in space. This is achieved by crowdsourcingmassive network data from mobile devices temporally and spatially. Aninstability analyzer reports base station stability and reachability toavoid getting stuck in a suboptimal network. The instability analyzermodels the decision logic and feeds this model with real configurationscollected directly from the device and indirectly from the serving cell,as well as dynamic environment settings created for various scenarios.For example, antenna parameters (pointing direction, frequency, andRSSI/TSSI and channel) are captured to identify optimal settings for aparticular device/client. The system can model cellular protocols isderived from the 5G standards for each protocol. This works particularlywell for non-moving client devices such as 5G modems/routers and mobilephones that operate within a house or office most of the time, forexample. When the mobile device is on the move, population data can beused to optimize antenna and communication parameters to derive theoptimal connection for the device or client. For example, the neuralnetwork layer can identify clients using the Ultra Reliable Low LatencyCommunications specification (such as full car automation, factoryautomation, and remote-controlled surgery where reliability andresponsiveness are mandatory) and control the 5G network to respond toURLLC requests by delivering data so quickly and reliably thatresponsiveness will be imperceptibly fast by selecting appropriateantenna parameters and settings for URLLC from the tower to the clientdevice.

In addition to the neural network plane, the logical functionarchitecture includes a data plane, a control plane, and a managementplane. The control plane includes a software defined topology (SDT)logical entity configured to establish a virtual data-plane logicaltopology for a service, a software defined resource allocation (SDRA)logical entity configured to map the virtual data-plane topology to aphysical data-plane for transporting service-related traffic over thewireless network, and a software defined per-service customized dataplane process (SDP) logical entity configured to select transportprotocol(s) for transporting the service-related traffic over a physicaldata-plane of the wireless network. The management plane may includeentities for performing various management related tasks. For example,the management plane may include an infrastructure management entityadapted to manage spectrum sharing between different radio accessnetworks (RANs) and/or different wireless networks, e.g., wirelessnetworks maintained by different operators. The management plane mayalso include one or more of a data and analytics entity, a customerservice management entity, a connectivity management entity, and acontent service management entity, which are described in greater detailbelow.

The neural network plane works with network functions virtualization(NFV) to design, deploy, and manage networking services. It is acomplementary approach to software-defined networking (SDN) for networkmanagement. While SDN separates the control and forwarding planes tooffer a centralized view of the network, NFV primarily focuses onoptimizing the network services themselves. The neural network planeautomates the optimization level to the next automation and efficiency.

A virtual service specific serving gateway (v-s-SGW) can be done. Thev-s-SGW is assigned specifically to a service being provided by a groupof wirelessly enabled devices, and is responsible for aggregatingservice-related traffic communicated by the group of wirelessly enableddevices. In an embodiment, the v-s-SGW provides access protection forthe service-related traffic by encrypting/decrypting data communicatedover bearer channels extending between the v-s-SGW and thewirelessly-enabled devices. The v-s-SGW may also provide a layer two(L2) anchor point between the group of wirelessly-enabled devices. Forexample, the v-s-SGW may provide convergence between the differentwireless communication protocols used by the wirelessly-enabled devices,as well as between different wireless networks and/or RANs being accessby the wirelessly-enabled devices. Additionally, the v-s-SGW may performat least some application layer processing for the service relatedtraffic communicated by the wirelessly-enabled devices. Aspects of thisdisclosure further provide an embodiment device naming structure. Forthe v-s-SGW. Specifically, a v-s-SGW initiated on a network device isassigned a local v-u-SGW ID. Outgoing packets from the v-u-SGW IDinclude the local v-u-SGW ID and a host ID of the network device.Accordingly, recipients of those outgoing packets can learn the localv-u-SGW ID and the host ID associated with a particular v-s-SGW, andthereafter send packets to the v-s-SGW by including the local v-u-SGW IDand the host ID in the packet header.

Location tracking as a service (LTaaS) can be provided. The LTaaSfeature may track locations of user equipment (UEs) via a devicelocation tracking as a service (LTaaS) layer such that the locations ofUEs are dynamically updated in a LTaaS layer as the UEs move todifferent locations in the wireless networks. In some embodiments, theLTaaS layer consists of a centralized control center. In otherembodiments, the LTaaS layer consists of a set of distributed controlcenters positioned in the wireless network, e.g., an applicationinstalled on a network device, such as a gateway or AP. In yet otherembodiments, the LTaaS layer comprises both a central controller centerand regional control centers. In such embodiments, the central controlcenter may be updated periodically by the regional control centers,which may monitor UE movement in their respective wireless networks. Inembodiments, the LTaaS layer may monitor general locations of the UEs.For example, the LTaaS layer may associate the UE's location with anetwork device in a specific wireless network, e.g., an access point, aserving gateway (SGW), etc.

Content may be cached in network devices of wireless network or radioaccess network (RAN) in anticipation that a mobile device or user willwant to access the content in the future. In some embodiments, a contentforwarding service manager (CFM) may select content to be pushed to acaching location in the wireless network based on the popularity ofavailable content stored in one or more application servers. The networkdevice may comprise a virtual information-centric networking (ICN)server of an ICN virtual network (VN), and may be adapted to provide thecached content to a virtual user-specific serving gateway (v-u-SGW) ofthe served user equipment (UE) upon request. Notably, the cached contentis stored by the network device in an information-centric networking(ICN) format, and the v-u-SGW may translate the cached content from theICN format to a user-specific format upon receiving the cached contentpursuant to a content request. The v-u-SGW may then relay the cachedcontent having the user-specific format to a served UE. After thecontent is pushed to the network device, the content forwarding servicemanager (CFM) may update a content cache table to indicate that thecontent has been cached at the network device. The content cache tablemay associate a name of the content with a network address of thenetwork device or the virtual IVN server included in the network device.The ICN VN may be transparent to the served UE, and may be operated byone of the wireless network operators or a third party. These and otheraspects are described in greater detail below.

The management plane 310 may include entities for performing variousmanagement related tasks. In this example, the management plane 330includes a data and analytics entity 311, an infrastructure managemententity 312, customer service management entity 313, a connectivitymanagement entity 314, and a content service management entity 315. Thedata and analytics entity 311 is configured to provide data analytics asa service (DAaaS). This may include manage on-demand network statusanalytics and on-demand service QoE status analytics for a particularservice, and providing a data analytics summary to a client. Theinfrastructure management entity 312 may manage spectrum sharing betweendifferent radio access network (RANs) in a wireless network, or betweenwireless networks maintained by different operators. This may includewireless network integration, management of RAN backhaul and access linkresources, coordination of spectrum sharing among co-located wirelessnetworks, access management, air interface management, and device accessnaming and network node naming responsibilities.

The customer service management entity 313 may provide customer servicefunctions, including managing customer context information,service-specific quality of experience (QoE) monitoring, and chargingresponsibilities. The connectivity management entity 314 may providelocation tracking as a service (LTaaS) over the data plane of thewireless network. The connectivity management entity 314 may also haveother responsibilities, such as establishing customized and scenarioaware location tracking scheme, establishing software defined andvirtual per-mobile user geographic location tracking schemes, andtriggering user specific data plane topology updates. The contentservice management entity 315 may manage content caching in the wirelessnetwork. This may include selecting content to be cached in RAN,selecting caching locations, configuring cache capable network nodes,and managing content forwarding. In some embodiments, the managementplane may also include a security management entity that is responsiblefor network access security (e.g., service-specific security, customerdevice network access protection, etc.), as well as inter-domain andintra-domain wireless network security.

The control plane 320 may include entities for performing variouscontrol related tasks. In this example, the control plane includes asoftware defined topology (SDT) logical entity 322, a software definedresource allocation (SDRA) logical entity 324, and a software definedper-service customized data plane process (SDP) logical entity 326. TheSDT entity 322, the SDRA logical entity 324, and the SDP logical entity326 may collectively configure a service-specific data plane forcarrying service-related traffic. More specifically, the softwaredefined topology (SDT) logical entity 322 is configured to establish avirtual data-plane logical topology for a service. This may includeselecting network devices to provide the service from a collection ofnetwork devices forming the data plane 330. The software definedresource allocation (SDRA) logical entity 324 is configured to map thevirtual data-plane topology to a physical data-plane for transportingservice-related traffic over the wireless network. This may includemapping logical links of the virtual data-plane topology to physicalpaths of the data plane. The software defined per-service customizeddata plane process (SDP) logical entity 326 is configured to selecttransport protocol(s) for transporting the service-related traffic overa physical data-plane of the wireless network. The transport protocolsmay be selected based on various criteria. In one example, the SDPlogical entity selects the transport protocol based on a characteristicof the service-related traffic, e.g., business characteristics, payloadvolume, quality of service (QoS) requirement, etc. In another example,the SDP logical entity selects the transport protocol based on acondition on the network, e.g., loading on the data paths, etc.

The SDT entity 322, the SDRA logical entity 324, and the SDP logicalentity 326 communicate with the neural network plane to optimize thesystem configuration (including antenna pointing/setting/redundancyassignment, among others), and they may also have other responsibilitiesbeyond their respective roles in establishing a service-specific dataplane. For example, the SDT entity 322 may dynamically define keyfunctionality for v-s-SGWs/v-u-SGWs, as well as enable mobile VNmigration and provide mobility management services. As another example,the SDRA logical entity 324 may embed virtual network sessions, as wellas provide radio transmission coordination. One or both of the SDTentity 322 and the SDRA logical entity 324 may provide policy andcharging rule function (PCRF) services.

The SDT entity 322, the SDRA logical entity 324, and the SDP logicalentity 326 may collectively configure a service-specific data plane forcarrying service-related traffic. Specifically, the SDT entity 322establishes a virtual data-plane logical topology for the service, theSDRA logical entity 324 maps the virtual data-plane topology to aphysical data-plane path for transporting service-related traffic overthe wireless network, and the SDP logical entity 326 select transportprotocol(s) for transporting the service-related traffic over thephysical data-plane.

In one example, the neural network can automatically allocate functionsin a mobile network based at least in part on utilization levels. Forexample, various components of the 5G network can include, but are notlimited to, a network exposure function (NEF), a network resourcefunction (NRF), an authentication server function (AUSF), an access andmobility management function (AMF), a policy control function (PCF), asession management function (SMF), a unified data management (UDM)function, a user plane function (UPF), and/or an application function(AF). For example, some or all of the functions discussed herein canprovide utilization levels, capability information, localityinformation, etc., associated with the various functions to a networkresource function (NRF) (or other component), for example, such that theNRF or other component can select a particular function of a pluralityof possible components providing the same function based on theutilization levels of the particular component. Thus, the system,devices, and techniques broadly apply to selecting network functions,and is not limited to a particular context or function, as discussedherein.

The neural network plane improves the functioning of a network by takinga global management view to optimize the network by reducing networkcongestion, dropped packets, or dropped calls due to overutilization ofresources. Further, the systems, devices, and techniques can reduce asize of components (e.g., processing capacity) by obviating or reducingany need to over-allocate resources to ensure spare capacity to reducecongestion. Further, selecting functions based on utilization levels canreduce signaling overhead associated with dynamically allocating a sizeof a virtual instance. In some instances, the architecture describedherein facilitates scalability to allow for additional components to beadded or removed while maintaining network performance. In someinstances, optimal functions can be selected in connection withhandovers (e.g., intracell or intercell) to balance a load on networkfunctions to provide improved Quality of Service (QoS) for networkcommunications. These and other improvements to the functioning of acomputer and network are discussed herein.

In one example, the neural network plane interacts with a user equipment(UE), an access and mobility management function (AMF), a networkresource function (NRF), a session management function (SMF), and a userplane function (UPF). The UE can transmit a registration request to theAMF. At a same or different time as the registration request, the UPFcan transmit utilization information to the NRF, which in turncommunicates with the neural network plane. In some instances, theutilization information can include information including, but notlimited to: CPU utilization level; memory utilization level; active orreserved bandwidth; a number of active sessions; a number of allowablesessions; historical usage; instantaneous usage; dropped packets; packetqueue size; delay; Quality of Service (QoS) level, antenna efficiency,antenna setting; and the like. Further, the utilization information caninclude a status of the UPF (e.g., online, offline, schedule formaintenance, etc.). In some instances, the UPF can transmit theutilization info at any regular or irregular interval. In someinstances, the UPF can transmit the utilization info in response to arequest from the NRF, and/or in response to a change in one or moreutilization levels above or below a threshold value.

Next, the UE can transmit a session request to the AMF, which in turncan transmit the session request to the SMF. In some instances, thesession request can include a request to initiate a voice communication,a video communication, a data communication, and the like, by andbetween the UE and other services or devices in the network. The SMF inturn talks to the neural network plane for management. Based on itslearned optimization, the neural network plane communicates instructionsto the SMF. At least partially in response to receiving command from theneural network plane, the SMF can transmit a UPF query to the NRF. Insome instances, the UPF query can include information including, but notlimited to: a type of session requested by the UE (e.g., voice, video,bandwidth, emergency, etc.); services requested by the UE; a location ofthe UE; a location of a destination of the session requested by the UE;a request for a single UPF or a plurality of UPFs; and the like.

In some instances, at least partially in response to receiving the UPFquery, the NRF can provide a UPF response to the SMF. In some instances,the UPF response can include one or more identifiers associated with oneor more UPFs that are available to provide services to the UE. In someinstances, the UPF response can be based at least in part on the sessionrequest and/or on the utilization info received from the UPF (as well asother UPFs, as discussed herein).

Based at least in part on the UPF response, the SMF can select a UPF(e.g., in a case where a plurality of UPF identifiers are provided tothe SMF) or can utilize the UPF provided by the NRF for a communicationsession. The SMF can select a UPF and can transmit a UPF selection tothe UPF that has been selected and/or designated to providecommunications to the UE.

At least partially in response to the UPF selection, the UPF can provideservices to the UE. As discussed herein, the UPF can facilitate datatransfer to and/or from the UE to facilitate communications such asvoice communications, video communications, data communications, etc. Inthis manner, the neural network plane incorporates intelligence inproviding services to requests in a way that optimizes system hardwareand software resources and overall cost.

Next, an example process is disclosed for selecting a network function,such as a user plane function, based on utilization information learnedby the neural network. The example process can be performed by theneural network in conjunction with the network resource function (NRF)(or another component), in connection with other components discussedherein. First, the neural network receives utilization informationassociated with one or more network functions, such as one or more userplanes. Although discussed in the context of a UPF, this process applyequally to other network functions, such as a network exposure function(NEF), a policy control function (PCF), a unified data management (UDM),an authentication server function (AUSF), an access and mobilitymanagement function (AMF), a session management function (SMF), anapplication function (AF), and the like. In one example, user planes ina network can transmit utilization information to the NRF. In someinstances, the NRF can request utilization information from various UPFs(or any network function) on a regular schedule, upon receipt of arequest to initiate a communication, and then forwarding suchinformation to the neural network plane for training, for example. Insome instances, the UPF (or any network function) can transmitutilization information upon determining that a utilization level haschanged more than a threshold amount compared to a previous utilizationlevel. In some instances, utilization information can include, but isnot limited to, one or more of: CPU utilization (e.g., % utilization),bandwidth utilization, memory utilization, number of allowable sessions,number of active sessions, historical utilization information, expectedutilization levels, latency, current QoS of active sessions, and thelike. Further, in some instances, the neural network can receivecapability information associated with the user plane(s) (or any networkfunction), location information associated with the user plane(s) (orany network function), etc. Such utilization information, capabilityinformation, location information, etc. can be stored in a databaseaccessible by the NRF.

Next, the process can include receiving a request for a networkfunction, such as a user plane, the request associated with a userequipment. For example, a request can be received from a sessionmanagement function (SMF) or an access and mobility management function(AMF) (or any network function) for a user plane (or any networkfunction) to initiate a communication for a user equipment. In someinstances, the request can indicate a number of user planes (or anynetwork function) to be provided by the NRF (e.g., one or many). In someinstances, the request can include information associated with thecommunication, such as a type of the communication, locations of the UEand/or the destination of the communication, specialized services (e.g.,video encoding, encryption, etc.) requested in association with thecommunication, a bandwidth of the communication, a minimum QoS of thecommunication, and the like. In some instances, the request can be basedat least in part on a request initiated by the UE and provided to theAMF, SMF, or any network function.

Operations by the neural network plane includes determining one or morenetwork functions (e.g., user planes) based at least in part on therequest and the utilization level. For example, the neural network planecan include determining that a first user plane (or any networkfunction) is associated with a first utilization level (e.g., 80% CPUutilization) and a second user plane (or any network function) isassociated with a second utilization level (e.g., 30% utilizationlevel). Further the neural network can include determining that thefirst utilization level is above a utilization threshold (e.g., 70% orany value) such that addition assignments of UEs to the UPF (or anynetwork function) may degrade the quality of connections associated withfirst UPF (or any network function). Accordingly, the neural network candetermine that the first UPF (or any network function) is to be selectedto provide data traffic for the UE.

As can be understood herein, there may be a variety of learningalgorithms or ways to determine which user planes (or any networkfunction) are to be selected as available for communication. In someinstances, the neural network can include determining that theutilization level of the second user plane (or any network function)(e.g., 30%, discussed above) is lower than the utilization level of thefirst user plane (or any network function) (e.g., 80%, discussed above),and accordingly, can determine that the second user plane (or anynetwork function) is to be selected for the communication.

The neural network determines a plurality of user planes (or any networkfunction) that are available for a communication (e.g., that have autilization level below a threshold value). In some instances, the userplanes (or any network function) can be selected based on a proximity tothe UE, capabilities requested by the UE, etc. In some instances, theoperation 506 can include ranking or prioritizing individual ones of theplurality of user planes (or any network function) as most appropriateto be selected for the communication. The neural network then providesan identification of the one or more user planes (or any networkfunction) to a session management function (SMF) (or any selectingnetwork function) to facilitate a communication with the user equipment.For example, the operation by the neural network can include providingan address or other identifier corresponding to one or more UPFs (or anyone or more network functions) to an SMF (or any selecting networkfunction) in the network. In the case where one user plane (or anynetwork function) is provided, the SMF (or any selecting networkfunction) may utilize the explicit user plane (or any network function)identified by the NRF. In the case where more than one user plane (orany network function) is provided, the identification may includeadditional information to allow the SMF (or any selecting networkfunction) to select a user plane (or any network function), as discussedherein.

In another example for selecting a user plane function based onutilization information during a handover performed by the neuralnetwork (or another component), in connection with other componentsdiscussed herein. As usual, the neural network has utilizationinformation associated with one or more user planes which provideutilization information to NRF that in turn sends the info to the neuralnetwork layer. Upon receiving a request for a user plane, the neuralnetwork plane can include providing a first selection of at least onefirst user plane based at least in part on the request and utilizationinformation. The operation can include the providing, allocating, and/orselecting at least one user plane based on utilization information tobalance a load across a plurality of available user planes. In someinstances, the operation 606 can include establishing a communicationfor the UE at a first radio access network (RAN) utilizing the firstuser plane. The neural network can receive an indication of a handoverrequest. For example, as a UE moves about an environment, a signalquality can decrease between the UE and the first RAN. Accordingly, theneural network can automatically change antenna parameters first basedon learned parameters, and if that does not change signal quality, theneural network can determine that a handover should occur, based on oneor more of, but not limited to: signal strength of an anchor connection(e.g., a signal strength of the first RAN); signal strength of a targetRAN (e.g., a signal strength of a second RAN); latency; UEspeed/direction; traffic level(s); QoS; etc. In some instances, theneural network determines that a new user plane is required/desiredbased at least in part on the indication of the handover request. Theneural network plane can provide a second selection of at least onesecond user plane based at least in part on the handover request andutilization information. For example, the at least one second user planecan include user planes suitable and available to facilitate acommunication with the UE. In some instances, the above operations canbe repeated as a UE moves about an environment (and/or in response toinitiate a handover based on UPF maintenance, for example). That is, theoperations can be repeated continuously or periodically to determine auser plane to facilitate a communication while balancing a load of theuser planes.

The neural network plane can automatically configure the direction ofantennas and combine antennas in a massive MIMO antenna by firstfocusing the antenna on the UE device (which optimizes thedirectionality of the wireless link between the BS and the UE), and thentransmitting first pilot signals via each of multiple antennas of theUE; receiving antenna combining information from a base station (BS),the antenna combining information for combining the multiple antennasinto one or more antenna groups and an orthogonal sequence allocated toeach of the one or more antenna groups; and transmitting second pilotsignals to the BS using the allocated orthogonal sequences, wherein thesecond pilot signals are used for estimating downlink channels from theBS to the UE, wherein the antenna combining information is determinedbased on correlation of each of the multiple antennas obtained from thefirst pilot signals, and wherein a same orthogonal sequence is appliedto a second pilot signal transmitted via one or more of the multipleantennas belonging to a same antenna group. The neural network can senda preferred antenna combination that is sent to the BS based on one ormore of the following: 1) minimize a correlation between effectivechannels of the one or more antenna groups, 2) an amount of data to betransmitted, 3) second pilot signals. The second pilot signals can becaptured during different time periods than a time period during which aUE of belonging to a second UE group transmits the second pilot signals.The 1st pilot signal can be transmitted by the UE even after the UEconfigure the antenna combination. In this case, the base station mayconfigure new antenna combination based on the previous antennacombination (mapping between one logical channel and another logicalchannel). Based on this, the base station may determine antennacombining information and transmit it to the UE and to make each of thelogical (effective) channels become orthogonal to each other. The neuralnetwork plane monitors performance and can automatically reconfigure ormodify antenna combination when the SINR of the received signals becomepoor over a predetermined period of time. Based on this request, thebase station may receive the antenna combining information again andtransmit it to the UE. The neural network plane may determine theantenna combining information to minimize the biggest correlation valuebetween the effective channels. Or, it may determine to make the biggestcorrelation value between the effective channels less than a thresholdvalue. By doing this, the base station may prevent the antenna groupsfrom being aligned in the same direction. In another example, supposethere are 2 UEs (UE a and UE b) and that the UE a has lots of data to betransmitted/received while there are little for UE b. In this case, theneural network provides more effective channels to UE a while UE b getsfewer number of effective channels. In another example, the UE maydetermine the preferred antenna combining method based on the ACK/NACKof the received data. When the number of effective channels increases,the more diversity gain can be acquired. So, the UE of this examplerequest more number of effective channels when the decoding results ofthe received data is NACK for certain number of time. Otherwise, the UEmay request less number of effective channels. In still another example,the UE may determine the preferred antenna combining method based on theestimated channel information. The above preferred antenna combiningmethods of the UE can be controlled and granted by the network. Theneural network may consider not only the UE transmitted this preferredantenna combining method, but other UEs within the cell.

In one implementation, FIG. 7B shows an exemplary learning machine toautomatically adjust the position/aim of the antennas to optimize datatransmission performance and/or coverage. As noted earlier, 4G systemshave range but lack speed. 5G systems have speed but require moreantennas and generally lack the range of 4G systems. To optimizeperformance, a learning machine is used to automatically track a mobiledevice and adjust the best arrangement for the antenna arrays. Theprocess is as follows:

Collect performance data from subsystems (see above) such as: Spatialand Modulation Symbols, RSSI, TSSI, CSI (channel state information), andattributes on channel matrix and error vector magnitude, for example

Extract features and train learning machine to optimize spectralefficiency and energy efficiency of the wireless system

During live communication, extract features from live 5G data and selectantenna orientation/setting/params based on client device, resourcesavailable, and tower network properties for optimum transmission.

FIGS. 7C-7D show exemplary learning machine details. While the learningmachine optimizes all resources, details on the antenna are discussednext, with the expectation that other resource allocations. The learningmachine turn the antenna arrays “smart” so that the best antenna linkagebetween transceivers is achieved. Further, when one of the antennaelements in the array fails, beamforming and beam steering performanceof the array degrades gracefully. Such an objective is achieved byreconfiguring the array when an element is found to be defective, byeither changing the material properties of the substrate or by applyingappropriate loading in order to make the array functional again. Oneembodiment changes the excitation coefficient for each array element(magnitude and phase) to optimize for changes due to the environmentsurrounding an array antenna. Using learning machines, one can train theantenna array to change its elements' phase or excitation distributionin order to maintain a certain radiation pattern or to enhance its beamsteering and nulling properties and solve the direction of arrival (DOA)as well.

The neural network control of the MIMO antennas provides significantgains that offer the ability to accommodate more users, at higher datarates, with better reliability, while consuming less power. Using neuralnetwork control of large number of antenna elements reduces power in agiven channel by focusing the energy to targeted mobile users usingprecoding techniques. By directing the wireless energy to specificusers, the power in channel is reduced and, at the same time,interference to other users is decreased.

In addition to controlling the 5G operation, the neural network can beused to provide local edge processing for IOT devices. A strikingfeature about neural networks is their enormous size. To reduce the sizeof the neural networks for edge learning while maintaining accuracy, thelocal neural network performs late down-sampling and filter countreduction, to get high performance at a low parameter count. Layers canbe removed or added to optimize the parameter efficiency of the network.In certain embodiments, the system can prune neurons to save some space,and a 50% reduction in network size has been done while retaining 97% ofthe accuracy. Further, edge devices on the other hand can be designed towork on 8 bit values, or less. Reducing precision can significantlyreduce the model size. For instance, reducing a 32 bit model to 8 bitmodel reduces model size. Since DRAM memory access is energy intensiveand slow, one embodiment keeps a small set of register files (about 1KB) to store local data that can be shared with 4 MACs as the leaningelements). Moreover, for video processing, frame image compression andsparsity in the graph and linear solver can be used to reduce the sizeof the local memory to avoid going to off chip DRAMs. For example, thelinear solver can use a non-zero Hessian memory array with a Choleskymodule as a linear solver.

In another embodiment, original full neural network can be trained inthe cloud, and distillation is used for teaching smaller networks usinga larger “teacher” network. Combined with transfer learning, this methodcan reduce model size without losing much accuracy. In one embodiment,the learning machine is supported by a GPU on a microprocessor, or toreconfigure the FPGA used as part of the baseband processing as neuralnetwork hardware.

It should also be appreciated that, while the antenna system of thepresent invention is primarily intended for 5G/6G systems, it can beused in space-borne communication applications, radar, as well as otherterrestrial applications, or in any application requiring a large,lightweight, stowable antenna.

Contemplated Variations

Even though the present disclosure has depicted and described vehicleshaving a particular structural configuration, it should be understood bythose skilled in the art that the vehicle 10 of the present disclosuremay alternatively have other structural configurations.

Even though the present disclosure has depicted and described thevehicle as a VTOL cabin having distributed propulsion systems withindependent propulsion assemblies on top and bottom of the vehicle, itshould be understood by those skilled in the art that the vehicle can bean aircraft with distributed propulsion systems with independentpropulsion assemblies attached to a frame using a combination of midwing configuration, high wing configuration and/or low wingconfiguration.

Even though the present disclosure has depicted and described thevehicle with distributed propulsion systems with independent propulsionassemblies having blades of a uniform design, it should be understood bythose skilled in the art that aircraft of the present disclosure mayhave distributed propulsion systems with independent propulsionassemblies having blades with different designs. The vehicle alternativecan be an aircraft with a vertical takeoff and landing mode and aforward flight mode.

Even though the present disclosure uses blades, it is contemplated thatprorotors can be used. A proprotor is a spinning airfoil that is used asboth an airplane-style propeller and a helicopter-style rotor during thesame flight. Proprotors are typically used on vertical takeoff andlanding (VTOL) aircraft. Proprotor blades with different designs can beused. As described herein, significantly more thrust is required duringvertical takeoff and landing as compared to forward flight. When maximumthrust is required during vertical takeoff and landing, all propulsionassemblies are operated with the larger proprotor blades with generallyhaving greater lift efficiency and enabling operations with heavierpayloads. When reduced thrust is required during forward flight,however, the larger propulsion assemblies could be shut down to conservepower with smaller propulsion assemblies operating to provide all therequired thrust, thereby increasing aircraft endurance. As discussedherein, when large propulsion assemblies are shut down, the associatedproprotor blades may passively fold or be feathered to reduce drag andfurther improve aircraft endurance. As an alternative or in addition tohaving proprotor blades of different length, proprotor blades of adistributed propulsion system of the present disclosure could also havedifferent blade twist, different angles of attack in fixed pitchembodiments, different pitch types such as a combination of fixed pitchand variable pitch prorotor blades, different blade shapes and the like.

Even though the present disclosure has depicted and described ahelicopter like design, the inventor contemplates that the vehicle canbe aircraft having straight wings, and it should be understood by thoseskilled in the art that aircraft of the present disclosure may havewings having alternate designs. The wings can be polyhedral wings withwing member having anhedral sections and dihedral sections. The aircraftcan have distributed propulsion systems with independent propulsionassemblies having proprotor with a uniform number of proprotor blades,but it should be understood by those skilled in the art that aircraft ofthe present disclosure may have distributed propulsion systems withindependent propulsion assemblies having proprotors with differentnumbers of blades When smaller propulsion assemblies are shut down, theassociated proprotor blades may passively fold or be feathered to reducedrag and improve aircraft endurance.

Advantages may include one or more of the following. The air vehicle cantravel point to point and can save energy relative to a car because theair vehicle does not need to travel on roads which may zigzag along theway to account for obstacles such as mountains. Further, once in theair, it takes less energy to move forward in the air than on the road.Navigation is simplified due to reduced obstacles. The vehicle is afresh start with all autonomous vehicles and the navigation system doesnot need to address legacy vehicles driven by less predictable humans.The system is also distributed to provide scalability to a level unheardof in aviation management, namely a control tower is local using eithera lead vehicle designated as control tower, or a lead wireless stationdesignated as the control tower, so that many more vehicles can bemanaged.

What is claimed is:
 1. A method for transporting people, comprisingproviding a cab having a moveable actuator coupled to the propulsionunit to move the propulsion unit between a first position above the cabduring lift-off and a second position during lateral flight; securing apassenger in the cab; determining a hand control gesture as captured bya plurality of cameras or sensors in the vehicle, wherein a sequence offinger, palm or hand movements represents a vehicle control request; anddetermining vehicle control options based on the model, a current stateof the vehicle and the environment of the vehicle.
 2. The method ofclaim 1, comprising, based on LIDAR, radar, and camera images,generating 3D models for navigation purposes.
 3. The method of claim 1,comprising crowd-sourcing 3D models and generating a high resolution 3Dmap of the region above the ground is generated.
 4. The method of claim1, comprising generating a multi-dimensional model of a vehicleoperating in a 3D environment.
 5. The method of claim 1, comprisingdetermining a hand control gesture as captured by a plurality of camerasor sensors in the vehicle.
 6. The method of claim 1, wherein a sequenceof finger, palm or hand movements represents a vehicle control request.7. The method of claim 1, comprising determining vehicle control optionsbased on the model, a current state of the vehicle and the environmentof the vehicle; and controlling the vehicle to operate based on themodel and the 3D environment.
 8. The method of claim 1, comprisingmapping system for an air space includes: a plurality of air vehicleseach having a plurality of environmental sensors; a processor in atleast one vehicle or in at least one communication tower (edgeprocessor) to receive sensor data and create a 3D model of the air spacefrom successive air vehicle sensor outputs.
 9. The method of claim 1,comprising providing processors and sensor located on 5G towers providelow latency edge processing capability, including machine learningprocessors to minimize cost of vehicle.
 10. The method of claim 1,comprising slice processing the network to provide dedicatedcommunications between vehicles.